ukb_bp_mood_prereg_overview
lina schaare
11/21/2019
0. Create reduced dataset from raw data
1. Load data
load("schaare/bp_mood_prereg/data/ukb_bp_mood_prereg_v3.RData")1.1 Remove participants without consent
p_excl <- read.csv("data/raw/w37721_20200820.csv", header = F)
p_excl <- p_excl$V1
idx <- na.exclude(match(p_excl,ukb$f.eid))
ukb = ukb[-idx,]2.0 Recode factor levels and add meaningful labels (Biobank format)
2.0.1 Recode covariates
## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(items)` instead of `items` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.2.1. Psychiatric Diagnosis
psychiatric diagnosis except for non-bipolar depression at initial assessment [http://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=20002]
##
## No Bipolar or Depression
## 89523
## Bipolar I Disorder
## 808
## Bipolar II Disorder
## 807
## Probable Recurrent major depression (severe)
## 8901
## Probable Recurrent major depression (moderate)
## 15010
## Single Probable major depression episode
## 7925
2.2. Neurological Disease
Stroke or any other neurological disease at initial assessment [http://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=6150, http://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=20002]
Stroke
##
## 0 1
## 368346 66812.3. Cardiovascular Disease
Cardiovascular disease at initial assessment
Heart attack
##
## 0 1
## 363524 11503Angina pectoris
##
## 0 1
## 358910 16117Diagnosed CHD (angina (1074), heart attack/MI (1075), heart failure/pulmonary odema (1076))
##
## 0 1
## 352083 22944Hypertension (at baseline and imaging visit)
##
## 0 1
## 365819 135745##
## 0 1
## 8835 28342.4. Any Disease
Systemic diseases (e.g. chronic obstructive pulmonary disease, renal disease) or malignant disease at initial assessment [http://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=20002]
2.6. Apply exclusion criteria (if enabled)
2.7. Medication
get medication coding from ukbiobank website: http://biobank.ctsu.ox.ac.uk/crystal/coding.cgi?id=4
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
## `summarise()` ungrouping output (override with `.groups` argument)
Calculate scale scores
Plots and descriptives of main outcomes and predictors
##
## Attaching package: 'scales'## The following objects are masked from 'package:psych':
##
## alpha, rescale## The following object is masked from 'package:purrr':
##
## discard## The following object is masked from 'package:readr':
##
## col_factor## Warning: Removed 454562 rows containing non-finite values (stat_bin).
## Warning: Removed 454562 rows containing non-finite values (stat_bin).## Time difference of 1619 days## Time difference of 1579.028 days## [1] 337.5848## Time difference of 0 days## Time difference of 2566 days## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 482150 rows containing non-finite values (stat_bin).
## Time difference of 3356 days## Time difference of 3261.404 days## [1] 638.438## Time difference of 1400 days## Time difference of 5043 days## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 454561 rows containing non-finite values (stat_bin).
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 448931 | 1.401826 | 0.5277209 | 1.25 | 1.299903 | 0.37065 | 1 | 4 | 3 | 2.005639 | 4.926962 | 0.0007876 |
## Warning: Removed 53563 rows containing non-finite values (stat_bin).
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 44648 | 1.301391 | 0.4375702 | 1.25 | 1.210113 | 0.37065 | 1 | 4 | 3 | 2.354004 | 7.540166 | 0.0020708 |
## Warning: Removed 457846 rows containing non-finite values (stat_bin).
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 154345 | 2.764605 | 3.695646 | 2 | 2.013039 | 2.9652 | 0 | 27 | 27 | 2.379164 | 7.411442 | 0.0094068 |
## Warning: Removed 348149 rows containing non-finite values (stat_bin).
##
## Pearson's product-moment correlation
##
## data: ukb$depr_c.0 and ukb$depr_c.2
## t = 128.1, df = 42301, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.5217917 0.5355236
## sample estimates:
## cor
## 0.5286922| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 172452 | 4.461741 | 0.5792737 | 4.5 | 4.479129 | 0.4942 | 1 | 6 | 5 | -0.5352529 | 1.63152 | 0.0013949 |
## Warning: Removed 330042 rows containing non-finite values (stat_bin).
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 47562 | 4.625391 | 0.5393972 | 4.666667 | 4.631407 | 0.4942 | 1 | 6 | 5 | -0.3619649 | 1.15853 | 0.0024733 |
## Warning: Removed 454932 rows containing non-finite values (stat_bin).
##
## Pearson's product-moment correlation
##
## data: ukb$wb.0 and ukb$wb.2
## t = 116.07, df = 16059, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.6669331 0.6837551
## sample estimates:
## cor
## 0.675432| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 456954 | 137.7773 | 18.62592 | 136 | 136.8752 | 18.5325 | 65 | 253.5 | 188.5 | 0.5292734 | 0.4744969 | 0.0275538 |
## Warning: Removed 45540 rows containing non-finite values (stat_bin).
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 36664 | 138.7126 | 18.59578 | 137.5 | 137.9285 | 18.5325 | 76.5 | 240.5 | 164 | 0.468306 | 0.4052764 | 0.0971168 |
## Warning: Removed 465830 rows containing non-finite values (stat_bin).
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 456966 | 82.19748 | 10.12344 | 82 | 81.97181 | 10.3782 | 36.5 | 147.5 | 111 | 0.2556898 | 0.2053863 | 0.0149757 |
## Warning: Removed 45540 rows containing non-finite values (stat_bin).
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 36668 | 78.68684 | 10.0203 | 78.5 | 78.50261 | 9.6369 | 37.5 | 124.5 | 87 | 0.2014313 | 0.0821564 | 0.0523284 |
## Warning: Removed 465826 rows containing non-finite values (stat_bin).
Descriptives
basic characterstics
## Warning in plotCI != "rect" || method %in% c("circle", "square"): 'length(x) = 4
## > 1' in coercion to 'logical(1)'
## Warning in plotCI != "rect" || method %in% c("circle", "square"): 'length(x) = 4
## > 1' in coercion to 'logical(1)'
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data## Warning in chisq.test(table(y, droplevels(dscrp$high_bp.0))): Chi-squared
## approximation may be incorrect| No diagnosed HTN or unknown (N=365819) |
Diagnosed HTN (N=135745) |
P-value / Effect size | Overall (N=502494) |
|
|---|---|---|---|---|
| Gender | ||||
| Female | 206978 (56.6%) | 65932 (48.6%) | <0.001 | 273378 (54.4%) |
| Male | 158841 (43.4%) | 69813 (51.4%) | 0.05 | 229115 (45.6%) |
| Missing | 0 (0%) | 0 (0%) | 1 (0.0%) | |
| Age (years) | ||||
| Mean (SD) | 55.4 (8.18) | 59.5 (7.09) | <0.001 | 56.5 (8.10) |
| Median [Min, Max] | 56.0 [38.0, 73.0] | 61.0 [39.0, 72.0] | 0.508 | 58.0 [37.0, 73.0] |
| Missing | 0 (0%) | 0 (0%) | 1 (0.0%) | |
| Household income (£) | ||||
| Prefer not to answer | 35523 (9.7%) | 14308 (10.5%) | <0.001 | 49844 (9.9%) |
| Do not know | 14353 (3.9%) | 6938 (5.1%) | 0.089 | 21304 (4.2%) |
| Less than 18,000 | 63105 (17.3%) | 34067 (25.1%) | 97196 (19.3%) | |
| 18,000 to 30,999 | 76534 (20.9%) | 31635 (23.3%) | 108174 (21.5%) | |
| 31,000 to 51,999 | 84504 (23.1%) | 26265 (19.3%) | 110771 (22.0%) | |
| 52,000 to 100,000 | 69329 (19.0%) | 16933 (12.5%) | 86263 (17.2%) | |
| Greater than 100,000 | 18983 (5.2%) | 3946 (2.9%) | 22929 (4.6%) | |
| Missing | 3488 (1.0%) | 1653 (1.2%) | 6013 (1.2%) | |
| Townsend deprivation index | ||||
| Mean (SD) | -1.38 (3.04) | -1.07 (3.21) | <0.001 | -1.29 (3.10) |
| Median [Min, Max] | -2.20 [-6.26, 11.0] | -1.95 [-6.26, 10.9] | 0.104 | -2.14 [-6.26, 11.0] |
| Missing | 446 (0.1%) | 175 (0.1%) | 624 (0.1%) | |
| Ethnic background | ||||
| Prefer not to answer | 1210 (0.3%) | 451 (0.3%) | <0.001 | 1661 (0.3%) |
| Do not know | 155 (0.0%) | 61 (0.0%) | 0.025 | 217 (0.0%) |
| White | 398 (0.1%) | 172 (0.1%) | 570 (0.1%) | |
| Mixed | 37 (0.0%) | 12 (0.0%) | 49 (0.0%) | |
| Asian or Asian British | 37 (0.0%) | 6 (0.0%) | 43 (0.0%) | |
| Black or Black British | 17 (0.0%) | 10 (0.0%) | 27 (0.0%) | |
| Chinese | 1255 (0.3%) | 318 (0.2%) | 1574 (0.3%) | |
| Other ethnic group | 3344 (0.9%) | 1213 (0.9%) | 4558 (0.9%) | |
| British | 322841 (88.3%) | 119719 (88.2%) | 442575 (88.1%) | |
| Irish | 9698 (2.7%) | 3507 (2.6%) | 13207 (2.6%) | |
| Any other white background | 12525 (3.4%) | 3805 (2.8%) | 16332 (3.3%) | |
| White and Black Caribbean | 456 (0.1%) | 164 (0.1%) | 620 (0.1%) | |
| White and Black African | 318 (0.1%) | 107 (0.1%) | 425 (0.1%) | |
| White and Asian | 653 (0.2%) | 178 (0.1%) | 831 (0.2%) | |
| Any other mixed background | 811 (0.2%) | 222 (0.2%) | 1033 (0.2%) | |
| Indian | 4157 (1.1%) | 1793 (1.3%) | 5951 (1.2%) | |
| Pakistani | 1370 (0.4%) | 463 (0.3%) | 1837 (0.4%) | |
| Bangladeshi | 174 (0.0%) | 62 (0.0%) | 236 (0.0%) | |
| Any other Asian background | 1308 (0.4%) | 506 (0.4%) | 1815 (0.4%) | |
| Caribbean | 2850 (0.8%) | 1667 (1.2%) | 4517 (0.9%) | |
| African | 2126 (0.6%) | 1265 (0.9%) | 3394 (0.7%) | |
| Any other Black background | 79 (0.0%) | 44 (0.0%) | 123 (0.0%) | |
| Missing | 0 (0%) | 0 (0%) | 899 (0.2%) | |
| Diabetes | ||||
| Prefer not to answer | 347 (0.1%) | 57 (0.0%) | <0.001 | 404 (0.1%) |
| Do not know | 726 (0.2%) | 554 (0.4%) | 0.135 | 1280 (0.3%) |
| No | 354961 (97.0%) | 118518 (87.3%) | 473479 (94.2%) | |
| Yes | 9785 (2.7%) | 16614 (12.2%) | 26399 (5.3%) | |
| Missing | 0 (0%) | 2 (0.0%) | 932 (0.2%) | |
| Smoking | ||||
| Prefer not to answer | 1431 (0.4%) | 624 (0.5%) | <0.001 | 2057 (0.4%) |
| Never | 205257 (56.1%) | 68244 (50.3%) | 0.047 | 273517 (54.4%) |
| Previous | 119175 (32.6%) | 53867 (39.7%) | 173051 (34.4%) | |
| Current | 39956 (10.9%) | 13010 (9.6%) | 52977 (10.5%) | |
| Missing | 0 (0%) | 0 (0%) | 892 (0.2%) | |
| Alcohol consumption | ||||
| Prefer not to answer | 440 (0.1%) | 164 (0.1%) | <0.001 | 604 (0.1%) |
| Daily or almost daily | 72888 (19.9%) | 28874 (21.3%) | 0.036 | 101768 (20.3%) |
| Three or four times a week | 86409 (23.6%) | 29023 (21.4%) | 115435 (23.0%) | |
| Once or twice a week | 96494 (26.4%) | 32788 (24.2%) | 129289 (25.7%) | |
| One to three times a month | 41491 (11.3%) | 14363 (10.6%) | 55855 (11.1%) | |
| Special occasions only | 40242 (11.0%) | 17760 (13.1%) | 58006 (11.5%) | |
| Never | 27855 (7.6%) | 12773 (9.4%) | 40639 (8.1%) | |
| Missing | 0 (0%) | 0 (0%) | 898 (0.2%) | |
| Physical activity (minutes/week) | ||||
| Mean (SD) | 2700 (2730) | 2510 (2660) | <0.001 | 2650 (2710) |
| Median [Min, Max] | 1820 [0, 19300] | 1630 [0, 19300] | 0.071 | 1770 [0, 19300] |
| Missing | 69495 (19.0%) | 29719 (21.9%) | 100122 (19.9%) | |
| Angina | ||||
| No diagnosed angina or unknown | 233569 (63.8%) | 124955 (92.1%) | <0.001 | 358910 (71.4%) |
| Diagnosed angina | 6735 (1.8%) | 9370 (6.9%) | 0.06 | 16117 (3.2%) |
| Missing | 125515 (34.3%) | 1420 (1.0%) | 127467 (25.4%) | |
| Heart attack | ||||
| No diagnosed heart attack or unknown | 234891 (64.2%) | 128249 (94.5%) | <0.001 | 363524 (72.3%) |
| Diagnosed heart attack | 5413 (1.5%) | 6076 (4.5%) | 0.039 | 11503 (2.3%) |
| Missing | 125515 (34.3%) | 1420 (1.0%) | 127467 (25.4%) | |
| Lifetime depression | ||||
| No diagnosed depression or unknown | 220783 (60.4%) | 125778 (92.7%) | <0.001 | 346919 (69.0%) |
| Diagnosed depression | 19521 (5.3%) | 8547 (6.3%) | 0.02 | 28108 (5.6%) |
| Missing | 125515 (34.3%) | 1420 (1.0%) | 127467 (25.4%) | |
| Heart rate (beats/min) | ||||
| Mean (SD) | 68.7 (10.7) | 70.9 (12.4) | <0.001 | 69.3 (11.2) |
| Median [Min, Max] | 68.0 [30.5, 173] | 70.0 [30.5, 170] | 0.192 | 68.5 [30.5, 173] |
| Missing | 32387 (8.9%) | 12732 (9.4%) | 45528 (9.1%) | |
| Systolic blood pressure (mmHg) | ||||
| Mean (SD) | 134 (17.6) | 147 (18.1) | <0.001 | 138 (18.6) |
| Median [Min, Max] | 133 [65.0, 253] | 146 [79.0, 254] | 0.725 | 136 [65.0, 254] |
| Missing | 32394 (8.9%) | 12737 (9.4%) | 45540 (9.1%) | |
| Diastolic blood pressure (mmHg) | ||||
| Mean (SD) | 80.7 (9.69) | 86.2 (10.2) | <0.001 | 82.2 (10.1) |
| Median [Min, Max] | 80.5 [36.5, 140] | 86.0 [43.5, 148] | 0.56 | 82.0 [36.5, 148] |
| Missing | 32387 (8.9%) | 12732 (9.4%) | 45528 (9.1%) | |
| BMI (kg/m2) | ||||
| Mean (SD) | 26.7 (4.41) | 29.4 (5.25) | <0.001 | 27.4 (4.80) |
| Median [Min, Max] | 26.1 [12.1, 69.0] | 28.6 [13.8, 74.7] | 0.584 | 26.7 [12.1, 74.7] |
| Missing | 1780 (0.5%) | 928 (0.7%) | 3105 (0.6%) | |
| No. antihypertensive medication | ||||
| Mean (SD) | 0.0842 (0.405) | 1.26 (1.14) | <0.001 | 0.403 (0.864) |
| Median [Min, Max] | 0 [0, 8.00] | 1.00 [0, 9.00] | 1.717 | 0 [0, 9.00] |
| No. antidepressant medication | ||||
| Mean (SD) | 0.0700 (0.266) | 0.101 (0.318) | <0.001 | 0.0784 (0.281) |
| Median [Min, Max] | 0 [0, 3.00] | 0 [0, 5.00] | 0.111 | 0 [0, 5.00] |
| Current depressive symptoms | ||||
| Mean (SD) | 1.39 (0.513) | 1.44 (0.564) | <0.001 | 1.40 (0.528) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] | 0.106 | 1.25 [1.00, 4.00] |
| Missing | 36900 (10.1%) | 15745 (11.6%) | 53563 (10.7%) | |
| Current depressive symptoms (2nd follow-up) | ||||
| Mean (SD) | 1.30 (0.434) | 1.33 (0.452) | <0.001 | 1.30 (0.438) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] | 0.07 | 1.25 [1.00, 4.00] |
| Missing | 330122 (90.2%) | 126806 (93.4%) | 457846 (91.1%) | |
| Well-being | ||||
| Mean (SD) | 4.48 (0.571) | 4.41 (0.598) | <0.001 | 4.46 (0.579) |
| Median [Min, Max] | 4.50 [1.00, 6.00] | 4.40 [1.00, 6.00] | 0.133 | 4.50 [1.00, 6.00] |
| Missing | 240332 (65.7%) | 88780 (65.4%) | 330042 (65.7%) | |
| Well-being (2nd follow-up) | ||||
| Mean (SD) | 4.64 (0.538) | 4.58 (0.543) | <0.001 | 4.63 (0.539) |
| Median [Min, Max] | 4.67 [1.00, 6.00] | 4.60 [1.00, 6.00] | 0.098 | 4.67 [1.00, 6.00] |
| Missing | 327883 (89.6%) | 126133 (92.9%) | 454932 (90.5%) | |
| PHQ-9 depressive symptoms (1st follow-up) | ||||
| Mean (SD) | 2.69 (3.61) | 3.03 (3.97) | <0.001 | 2.76 (3.70) |
| Median [Min, Max] | 2.00 [0, 27.0] | 2.00 [0, 27.0] | 0.092 | 2.00 [0, 27.0] |
| Missing | 245127 (67.0%) | 102166 (75.3%) | 348149 (69.3%) | |
| Diagnosed hypertension | ||||
| No diagnosed HTN or unknown | 365819 (100%) | 0 (0%) | NA | 365819 (72.8%) |
| Diagnosed HTN | 0 (0%) | 135745 (100%) | NaN | 135745 (27.0%) |
| P-value / Effect size | 0 (0%) | 0 (0%) | 0 (0%) | |
| Missing | 0 (0%) | 0 (0%) | 930 (0.2%) |
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data
## Warning in cohen.d.default(d, f, subject = subject, ...): Factor with multiple
## levels, using only the two actually present in data## Warning in chisq.test(table(y, droplevels(dscrp$high_bp.0))): Chi-squared
## approximation may be incorrect| No diagnosed HTN or unknown (N=365819) |
Diagnosed HTN (N=135745) |
P-value / Effect size | Overall (N=502494) |
|
|---|---|---|---|---|
| Gender | ||||
| Female | 206978 (56.6%) | 65932 (48.6%) | <0.001 | 273378 (54.4%) |
| Male | 158841 (43.4%) | 69813 (51.4%) | 0.05 | 229115 (45.6%) |
| Missing | 0 (0%) | 0 (0%) | 1 (0.0%) | |
| Age (years) | ||||
| Mean (SD) | 55.4 (8.18) | 59.5 (7.09) | <0.001 | 56.5 (8.10) |
| Median [Min, Max] | 56.0 [38.0, 73.0] | 61.0 [39.0, 72.0] | 0.508 | 58.0 [37.0, 73.0] |
| Missing | 0 (0%) | 0 (0%) | 1 (0.0%) | |
| Townsend deprivation index | ||||
| Mean (SD) | -1.38 (3.04) | -1.07 (3.21) | <0.001 | -1.29 (3.10) |
| Median [Min, Max] | -2.20 [-6.26, 11.0] | -1.95 [-6.26, 10.9] | 0.104 | -2.14 [-6.26, 11.0] |
| Missing | 446 (0.1%) | 175 (0.1%) | 624 (0.1%) | |
| Systolic blood pressure (mmHg) | ||||
| Mean (SD) | 134 (17.6) | 147 (18.1) | <0.001 | 138 (18.6) |
| Median [Min, Max] | 133 [65.0, 253] | 146 [79.0, 254] | 0.725 | 136 [65.0, 254] |
| Missing | 32394 (8.9%) | 12737 (9.4%) | 45540 (9.1%) | |
| Diastolic blood pressure (mmHg) | ||||
| Mean (SD) | 80.7 (9.69) | 86.2 (10.2) | <0.001 | 82.2 (10.1) |
| Median [Min, Max] | 80.5 [36.5, 140] | 86.0 [43.5, 148] | 0.56 | 82.0 [36.5, 148] |
| Missing | 32387 (8.9%) | 12732 (9.4%) | 45528 (9.1%) | |
| Heart rate (beats/min) | ||||
| Mean (SD) | 68.7 (10.7) | 70.9 (12.4) | <0.001 | 69.3 (11.2) |
| Median [Min, Max] | 68.0 [30.5, 173] | 70.0 [30.5, 170] | 0.192 | 68.5 [30.5, 173] |
| Missing | 32387 (8.9%) | 12732 (9.4%) | 45528 (9.1%) | |
| BMI (kg/m2) | ||||
| Mean (SD) | 26.7 (4.41) | 29.4 (5.25) | <0.001 | 27.4 (4.80) |
| Median [Min, Max] | 26.1 [12.1, 69.0] | 28.6 [13.8, 74.7] | 0.584 | 26.7 [12.1, 74.7] |
| Missing | 1780 (0.5%) | 928 (0.7%) | 3105 (0.6%) | |
| Diabetes | ||||
| Prefer not to answer | 347 (0.1%) | 57 (0.0%) | <0.001 | 404 (0.1%) |
| Do not know | 726 (0.2%) | 554 (0.4%) | 0.135 | 1280 (0.3%) |
| No | 354961 (97.0%) | 118518 (87.3%) | 473479 (94.2%) | |
| Yes | 9785 (2.7%) | 16614 (12.2%) | 26399 (5.3%) | |
| Missing | 0 (0%) | 2 (0.0%) | 932 (0.2%) | |
| Angina | ||||
| No diagnosed angina or unknown | 233569 (63.8%) | 124955 (92.1%) | <0.001 | 358910 (71.4%) |
| Diagnosed angina | 6735 (1.8%) | 9370 (6.9%) | 0.06 | 16117 (3.2%) |
| Missing | 125515 (34.3%) | 1420 (1.0%) | 127467 (25.4%) | |
| Heart attack | ||||
| No diagnosed heart attack or unknown | 234891 (64.2%) | 128249 (94.5%) | <0.001 | 363524 (72.3%) |
| Diagnosed heart attack | 5413 (1.5%) | 6076 (4.5%) | 0.039 | 11503 (2.3%) |
| Missing | 125515 (34.3%) | 1420 (1.0%) | 127467 (25.4%) | |
| Lifetime depression | ||||
| No diagnosed depression or unknown | 220783 (60.4%) | 125778 (92.7%) | <0.001 | 346919 (69.0%) |
| Diagnosed depression | 19521 (5.3%) | 8547 (6.3%) | 0.02 | 28108 (5.6%) |
| Missing | 125515 (34.3%) | 1420 (1.0%) | 127467 (25.4%) | |
| No. antihypertensive medication | ||||
| Mean (SD) | 0.0842 (0.405) | 1.26 (1.14) | <0.001 | 0.403 (0.864) |
| Median [Min, Max] | 0 [0, 8.00] | 1.00 [0, 9.00] | 1.717 | 0 [0, 9.00] |
| No. antidepressant medication | ||||
| Mean (SD) | 0.0700 (0.266) | 0.101 (0.318) | <0.001 | 0.0784 (0.281) |
| Median [Min, Max] | 0 [0, 3.00] | 0 [0, 5.00] | 0.111 | 0 [0, 5.00] |
| Current depressive symptoms | ||||
| Mean (SD) | 1.39 (0.513) | 1.44 (0.564) | <0.001 | 1.40 (0.528) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] | 0.106 | 1.25 [1.00, 4.00] |
| Missing | 36900 (10.1%) | 15745 (11.6%) | 53563 (10.7%) | |
| Well-being | ||||
| Mean (SD) | 4.48 (0.571) | 4.41 (0.598) | <0.001 | 4.46 (0.579) |
| Median [Min, Max] | 4.50 [1.00, 6.00] | 4.40 [1.00, 6.00] | 0.133 | 4.50 [1.00, 6.00] |
| Missing | 240332 (65.7%) | 88780 (65.4%) | 330042 (65.7%) | |
| Diagnosed hypertension | ||||
| No diagnosed HTN or unknown | 365819 (100%) | 0 (0%) | NA | 365819 (72.8%) |
| Diagnosed HTN | 0 (0%) | 135745 (100%) | NaN | 135745 (27.0%) |
| P-value / Effect size | 0 (0%) | 0 (0%) | 0 (0%) | |
| Missing | 0 (0%) | 0 (0%) | 930 (0.2%) |
| No diagnosed HTN or unknown (N=8835) |
Diagnosed HTN (N=2834) |
Overall (N=47933) |
|
|---|---|---|---|
| Gender | |||
| Female | 4771 (54.0%) | 1242 (43.8%) | 24793 (51.7%) |
| Male | 4064 (46.0%) | 1592 (56.2%) | 23140 (48.3%) |
| Age (years) | |||
| Mean (SD) | 61.4 (7.59) | 64.3 (6.88) | 64.1 (7.70) |
| Median [Min, Max] | 62.0 [44.0, 79.0] | 65.0 [45.0, 79.0] | 65.0 [44.0, 82.0] |
| Systolic blood pressure (mmHg) | |||
| Mean (SD) | 133 (16.7) | 145 (17.1) | 139 (18.6) |
| Median [Min, Max] | 132 [89.5, 213] | 144 [76.5, 219] | 138 [76.5, 241] |
| Missing | 514 (5.8%) | 153 (5.4%) | 11269 (23.5%) |
| Diastolic blood pressure (mmHg) | |||
| Mean (SD) | 77.5 (9.63) | 82.7 (9.77) | 78.7 (10.0) |
| Median [Min, Max] | 77.0 [45.0, 119] | 82.5 [46.5, 123] | 78.5 [37.5, 125] |
| Missing | 514 (5.8%) | 153 (5.4%) | 11265 (23.5%) |
| Current depressive symptoms (2nd follow-up) | |||
| Mean (SD) | 1.30 (0.442) | 1.32 (0.450) | 1.30 (0.438) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] |
| Missing | 531 (6.0%) | 174 (6.1%) | 3285 (6.9%) |
| Well-being (2nd follow-up) | |||
| Mean (SD) | 4.62 (0.546) | 4.58 (0.543) | 4.63 (0.539) |
| Median [Min, Max] | 4.67 [1.75, 6.00] | 4.60 [1.60, 6.00] | 4.67 [1.00, 6.00] |
| Missing | 4 (0.0%) | 0 (0%) | 371 (0.8%) |
| Diagnosed hypertension (2nd follow-up) | |||
| No diagnosed HTN or unknown | 8835 (100%) | 0 (0%) | 8835 (18.4%) |
| Diagnosed HTN | 0 (0%) | 2834 (100%) | 2834 (5.9%) |
| Missing | 0 (0%) | 0 (0%) | 36264 (75.7%) |
Label variables with meaningful names
##
## Attaching package: 'expss'## The following objects are masked from 'package:stringr':
##
## fixed, regex## The following objects are masked from 'package:dplyr':
##
## between, compute, contains, first, last, na_if, recode, vars## The following objects are masked from 'package:purrr':
##
## keep, modify, modify_if, transpose, when## The following objects are masked from 'package:tidyr':
##
## contains, nest## The following object is masked from 'package:ggplot2':
##
## varsadditional characteristics
##
## Female Male
## No diagnosed HTN or unknown 206978 158841
## Diagnosed HTN 65932 69813##
## Prefer not to answer Do not know No Yes
## No diagnosed HTN or unknown 347 726 354961 9785
## Diagnosed HTN 57 554 118518 16614##
## No diagnosed angina or unknown Diagnosed angina
## No diagnosed HTN or unknown 233569 6735
## Diagnosed HTN 124955 9370##
## No diagnosed heart attack or unknown
## No diagnosed HTN or unknown 234891
## Diagnosed HTN 128249
##
## Diagnosed heart attack
## No diagnosed HTN or unknown 5413
## Diagnosed HTN 6076##
## No diagnosed depression or unknown
## No diagnosed HTN or unknown 220783
## Diagnosed HTN 125778
##
## Diagnosed depression
## No diagnosed HTN or unknown 19521
## Diagnosed HTN 8547##
## Prefer not to answer Do not know
## 1661 217
## White Mixed
## 570 49
## Asian or Asian British Black or Black British
## 43 27
## Chinese Other ethnic group
## 1574 4558
## British Irish
## 442575 13207
## Any other white background White and Black Caribbean
## 16332 620
## White and Black African White and Asian
## 425 831
## Any other mixed background Indian
## 1033 5951
## Pakistani Bangladeshi
## 1837 236
## Any other Asian background Caribbean
## 1815 4517
## African Any other Black background
## 3394 123##
## Prefer not to answer Do not know Less than 18,000
## 49844 21304 97196
## 18,000 to 30,999 31,000 to 51,999 52,000 to 100,000
## 108174 110771 86263
## Greater than 100,000
## 22929| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 47933 | 64.06839 | 7.70439 | 65 | 64.18166 | 8.8956 | 44 | 82 | 38 | -0.1339123 | -0.8360349 | 0.0351901 |
| Female (N=24793) |
Male (N=23140) |
Overall (N=47933) |
|
|---|---|---|---|
| Gender | |||
| Female | 24793 (100%) | 0 (0%) | 24793 (51.7%) |
| Male | 0 (0%) | 23140 (100%) | 23140 (48.3%) |
| Age (years) | |||
| Mean (SD) | 63.4 (7.56) | 64.8 (7.79) | 64.1 (7.70) |
| Median [Min, Max] | 64.0 [45.0, 82.0] | 66.0 [44.0, 82.0] | 65.0 [44.0, 82.0] |
Conceptual replication analyses:
with confounders (cf. Herrmann-Lingen et al.): age (f.21003), sex (f.31), history of CHD (angina), myocardial infarction (heartattack), diabetes (f.2443), depression (depr_l), BMI (f.21001), resting heart rate (mean_hr)
Cross-sectional regressions of Systolic Blood Pressure, Hypertension and Number of Antihypertensive Medications With Well-Being and Depressive Mood:
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5356 -0.5959 -0.2749 0.3707 5.1654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.828e-15 1.697e-03 0.00 1
## f.21003.0.0 -1.690e-01 1.722e-03 -98.13 <2e-16 ***
## f.31.0.0 -5.334e-02 1.728e-03 -30.86 <2e-16 ***
## angina.0 6.602e-02 1.821e-03 36.26 <2e-16 ***
## heartattack.0 2.463e-02 1.816e-03 13.56 <2e-16 ***
## f.2443.0.0 3.176e-02 1.747e-03 18.18 <2e-16 ***
## depr_l.0 2.662e-01 1.707e-03 155.95 <2e-16 ***
## f.21001.0.0 7.036e-02 1.753e-03 40.14 <2e-16 ***
## mean_hr.0 5.221e-02 1.740e-03 30.01 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9352 on 303762 degrees of freedom
## Multiple R-squared: 0.1254, Adjusted R-squared: 0.1254
## F-statistic: 5446 on 8 and 303762 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit1 <- lm(mdl, data=dat_scaled)
summary(mdl_fit1)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5083 -0.5955 -0.2715 0.3691 5.1697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.980e-15 1.693e-03 0.000 1.0000
## mean_sbp.0 -6.344e-02 1.913e-03 -33.170 <2e-16 ***
## high_bp.0 4.308e-02 2.228e-03 19.341 <2e-16 ***
## htn_meds_count.0 -5.967e-03 2.371e-03 -2.516 0.0119 *
## f.21003.0.0 -1.557e-01 1.844e-03 -84.438 <2e-16 ***
## f.31.0.0 -4.713e-02 1.744e-03 -27.028 <2e-16 ***
## angina.0 6.286e-02 1.879e-03 33.456 <2e-16 ***
## heartattack.0 2.185e-02 1.858e-03 11.757 <2e-16 ***
## f.2443.0.0 2.700e-02 1.771e-03 15.250 <2e-16 ***
## depr_l.0 2.641e-01 1.706e-03 154.831 <2e-16 ***
## f.21001.0.0 7.226e-02 1.811e-03 39.905 <2e-16 ***
## mean_hr.0 5.432e-02 1.745e-03 31.131 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9332 on 303759 degrees of freedom
## Multiple R-squared: 0.1291, Adjusted R-squared: 0.1291
## F-statistic: 4094 on 11 and 303759 DF, p-value: < 2.2e-16car::vif(mdl_fit1)## Registered S3 methods overwritten by 'car':
## method from
## influence.merMod lme4
## cooks.distance.influence.merMod lme4
## dfbeta.influence.merMod lme4
## dfbetas.influence.merMod lme4## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.276043 1.730777 1.961524 1.185724
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.060559 1.231209 1.204684 1.093477
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.014951 1.143546 1.061880# calculate delta adj. r squared
summary(mdl_fit1)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.00367564### lifetime depression
outcome <- "depr_l.0"
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "f.21001.0.0", "mean_hr.0") # without depr_l.0
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, depr_l.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8716 -0.3453 -0.2680 -0.1781 3.7843
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.705e-14 1.703e-03 0.000 1.00000
## f.21003.0.0 -7.543e-02 1.722e-03 -43.795 < 2e-16 ***
## f.31.0.0 -6.456e-02 1.730e-03 -37.315 < 2e-16 ***
## angina.0 1.507e-02 1.828e-03 8.244 < 2e-16 ***
## heartattack.0 -1.658e-03 1.824e-03 -0.909 0.36336
## f.2443.0.0 -4.896e-03 1.750e-03 -2.797 0.00516 **
## f.21001.0.0 3.789e-02 1.756e-03 21.580 < 2e-16 ***
## mean_hr.0 1.402e-02 1.744e-03 8.040 9.03e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9942 on 340892 degrees of freedom
## Multiple R-squared: 0.0116, Adjusted R-squared: 0.01158
## F-statistic: 571.3 on 7 and 340892 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit2 <- lm(mdl, data=dat_scaled)
summary(mdl_fit2)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9246 -0.3528 -0.2653 -0.1714 3.9399
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.713e-14 1.701e-03 0.000 1.00000
## mean_sbp.0 -5.108e-02 1.915e-03 -26.672 < 2e-16 ***
## high_bp.0 -8.755e-03 2.233e-03 -3.921 8.82e-05 ***
## htn_meds_count.0 -6.485e-03 2.378e-03 -2.727 0.00639 **
## f.21003.0.0 -5.676e-02 1.848e-03 -30.720 < 2e-16 ***
## f.31.0.0 -5.693e-02 1.747e-03 -32.592 < 2e-16 ***
## angina.0 1.493e-02 1.890e-03 7.898 2.85e-15 ***
## heartattack.0 -3.243e-03 1.866e-03 -1.738 0.08228 .
## f.2443.0.0 -4.814e-03 1.775e-03 -2.712 0.00668 **
## f.21001.0.0 4.845e-02 1.815e-03 26.691 < 2e-16 ***
## mean_hr.0 1.785e-02 1.750e-03 10.198 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9929 on 340889 degrees of freedom
## Multiple R-squared: 0.0142, Adjusted R-squared: 0.01417
## F-statistic: 491.2 on 10 and 340889 DF, p-value: < 2.2e-16car::vif(mdl_fit2)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.268498 1.723945 1.955645 1.180631
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.055064 1.235423 1.204583 1.089335
## f.21001.0.0 mean_hr.0
## 1.139237 1.059392# calculate delta adj. r squared
summary(mdl_fit2)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.00259916### well-being
outcome <- "wb.0"
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, wb.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2972 -0.5988 0.0220 0.6339 4.1065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.846e-16 2.656e-03 0.000 1
## f.21003.0.0 1.886e-01 2.691e-03 70.079 <2e-16 ***
## f.31.0.0 -2.475e-02 2.703e-03 -9.158 <2e-16 ***
## angina.0 -5.272e-02 2.825e-03 -18.663 <2e-16 ***
## heartattack.0 -2.639e-02 2.819e-03 -9.362 <2e-16 ***
## f.2443.0.0 -4.253e-02 2.731e-03 -15.575 <2e-16 ***
## depr_l.0 -1.673e-01 2.671e-03 -62.651 <2e-16 ***
## f.21001.0.0 -8.279e-02 2.741e-03 -30.205 <2e-16 ***
## mean_hr.0 -4.938e-02 2.725e-03 -18.122 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.957 on 129867 degrees of freedom
## Multiple R-squared: 0.08414, Adjusted R-squared: 0.08409
## F-statistic: 1491 on 8 and 129867 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit3 <- lm(mdl, data=dat_scaled)
summary(mdl_fit3)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3564 -0.5941 0.0234 0.6315 4.0762
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.014e-16 2.650e-03 0.000 1.0000
## mean_sbp.0 5.672e-02 2.987e-03 18.990 <2e-16 ***
## high_bp.0 -5.695e-02 3.504e-03 -16.254 <2e-16 ***
## htn_meds_count.0 7.425e-03 3.713e-03 2.000 0.0455 *
## f.21003.0.0 1.787e-01 2.884e-03 61.979 <2e-16 ***
## f.31.0.0 -2.769e-02 2.720e-03 -10.179 <2e-16 ***
## angina.0 -4.940e-02 2.911e-03 -16.967 <2e-16 ***
## heartattack.0 -2.379e-02 2.884e-03 -8.248 <2e-16 ***
## f.2443.0.0 -3.687e-02 2.766e-03 -13.331 <2e-16 ***
## depr_l.0 -1.658e-01 2.668e-03 -62.149 <2e-16 ***
## f.21001.0.0 -8.132e-02 2.836e-03 -28.679 <2e-16 ***
## mean_hr.0 -4.986e-02 2.732e-03 -18.253 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.955 on 129864 degrees of freedom
## Multiple R-squared: 0.08804, Adjusted R-squared: 0.08796
## F-statistic: 1140 on 11 and 129864 DF, p-value: < 2.2e-16car::vif(mdl_fit3)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.270374 1.747935 1.963056 1.184353
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.053750 1.207105 1.184366 1.089366
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.013835 1.144947 1.062592# calculate delta adj. r squared
summary(mdl_fit3)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.003875323Additional covariates
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0, f.738.0.0, education.0, ethn.0, f.1200.0.0, f.54.0.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
dat$f.738.0.0 <- as.numeric(dat$f.738.0.0)
# define predictors and covariates
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0", "f.738.0.0", "education.0", "ethn.0", "f.1200.0.0", "f.54.0.0")
### depressive mood
outcome <- "depr_c.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0, f.738.0.0, education.0, ethn.0, f.1200.0.0, f.54.0.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9719 -0.5703 -0.2095 0.3641 5.6662
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.999e-15 1.814e-03 0.000 1
## f.21003.0.0 -2.069e-01 1.916e-03 -107.960 < 2e-16 ***
## f.31.0.0 -1.307e-02 1.882e-03 -6.944 3.82e-12 ***
## angina.0 4.435e-02 1.934e-03 22.933 < 2e-16 ***
## heartattack.0 1.443e-02 1.928e-03 7.484 7.24e-14 ***
## f.2443.0.0 1.802e-02 1.869e-03 9.642 < 2e-16 ***
## depr_l.0 2.471e-01 1.833e-03 134.821 < 2e-16 ***
## f.21001.0.0 5.525e-02 1.879e-03 29.402 < 2e-16 ***
## mean_hr.0 4.116e-02 1.866e-03 22.060 < 2e-16 ***
## f.738.0.0 -8.314e-02 1.905e-03 -43.647 < 2e-16 ***
## education.0 -7.679e-03 1.821e-03 -4.218 2.47e-05 ***
## ethn.0 -5.942e-02 1.853e-03 -32.072 < 2e-16 ***
## f.1200.0.0 2.257e-01 1.845e-03 122.364 < 2e-16 ***
## f.54.0.0 1.364e-02 1.830e-03 7.451 9.31e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9021 on 247257 degrees of freedom
## Multiple R-squared: 0.1863, Adjusted R-squared: 0.1862
## F-statistic: 4354 on 13 and 247257 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit1_covs <- lm(mdl, data=dat_scaled)
summary(mdl_fit1_covs)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9525 -0.5700 -0.2087 0.3631 5.6092
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.146e-15 1.811e-03 0.000 1.00000
## mean_sbp.0 -6.191e-02 2.064e-03 -30.001 < 2e-16 ***
## high_bp.0 3.395e-02 2.403e-03 14.129 < 2e-16 ***
## htn_meds_count.0 -1.072e-02 2.515e-03 -4.262 2.03e-05 ***
## f.21003.0.0 -1.919e-01 2.039e-03 -94.121 < 2e-16 ***
## f.31.0.0 -5.749e-03 1.903e-03 -3.021 0.00252 **
## angina.0 4.285e-02 1.983e-03 21.608 < 2e-16 ***
## heartattack.0 1.278e-02 1.970e-03 6.484 8.95e-11 ***
## f.2443.0.0 1.501e-02 1.894e-03 7.923 2.33e-15 ***
## depr_l.0 2.448e-01 1.832e-03 133.637 < 2e-16 ***
## f.21001.0.0 6.028e-02 1.942e-03 31.039 < 2e-16 ***
## mean_hr.0 4.396e-02 1.872e-03 23.481 < 2e-16 ***
## f.738.0.0 -8.417e-02 1.902e-03 -44.256 < 2e-16 ***
## education.0 -7.266e-03 1.817e-03 -3.999 6.38e-05 ***
## ethn.0 -5.870e-02 1.850e-03 -31.725 < 2e-16 ***
## f.1200.0.0 2.244e-01 1.842e-03 121.800 < 2e-16 ***
## f.54.0.0 1.285e-02 1.827e-03 7.032 2.04e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9004 on 247254 degrees of freedom
## Multiple R-squared: 0.1894, Adjusted R-squared: 0.1893
## F-statistic: 3610 on 16 and 247254 DF, p-value: < 2.2e-16car::vif(mdl_fit1_covs)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.298781 1.761064 1.929876 1.268104
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.104384 1.199361 1.184262 1.094040
## depr_l.0 f.21001.0.0 mean_hr.0 f.738.0.0
## 1.023517 1.150286 1.068933 1.103389
## education.0 ethn.0 f.1200.0.0 f.54.0.0
## 1.007142 1.044361 1.035100 1.018354# calculate delta adj. r squared
summary(mdl_fit1_covs)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.003077418### well-being
outcome <- "wb.0"
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0", "f.738.0.0", "education.0", "ethn.0", "f.1200.0.0", "f.54.0.0")
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, wb.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0, f.738.0.0, education.0, ethn.0, f.1200.0.0, f.54.0.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2106 -0.5850 0.0155 0.6068 4.6568
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.028e-14 2.856e-03 0.000 1
## f.21003.0.0 2.123e-01 3.000e-03 70.765 < 2e-16 ***
## f.31.0.0 -6.538e-02 2.958e-03 -22.099 < 2e-16 ***
## angina.0 -4.050e-02 3.019e-03 -13.413 < 2e-16 ***
## heartattack.0 -2.023e-02 3.012e-03 -6.718 1.86e-11 ***
## f.2443.0.0 -3.095e-02 2.948e-03 -10.496 < 2e-16 ***
## depr_l.0 -1.504e-01 2.885e-03 -52.148 < 2e-16 ***
## f.21001.0.0 -7.592e-02 2.959e-03 -25.662 < 2e-16 ***
## mean_hr.0 -3.587e-02 2.940e-03 -12.200 < 2e-16 ***
## f.738.0.0 1.155e-01 2.986e-03 38.679 < 2e-16 ***
## education.0 2.945e-02 2.873e-03 10.249 < 2e-16 ***
## ethn.0 6.674e-02 2.975e-03 22.435 < 2e-16 ***
## f.1200.0.0 -1.730e-01 2.903e-03 -59.571 < 2e-16 ***
## f.54.0.0 -4.461e-02 2.911e-03 -15.325 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9306 on 106114 degrees of freedom
## Multiple R-squared: 0.1342, Adjusted R-squared: 0.1341
## F-statistic: 1265 on 13 and 106114 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit3_covs <- lm(mdl, data=dat_scaled)
summary(mdl_fit3_covs)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1088 -0.5864 0.0157 0.6061 4.6478
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.903e-14 2.852e-03 0.000 1.00000
## mean_sbp.0 5.506e-02 3.247e-03 16.954 < 2e-16 ***
## high_bp.0 -4.470e-02 3.810e-03 -11.732 < 2e-16 ***
## htn_meds_count.0 1.180e-02 3.974e-03 2.969 0.00299 **
## f.21003.0.0 2.005e-01 3.200e-03 62.658 < 2e-16 ***
## f.31.0.0 -6.972e-02 2.985e-03 -23.356 < 2e-16 ***
## angina.0 -3.896e-02 3.095e-03 -12.589 < 2e-16 ***
## heartattack.0 -1.881e-02 3.077e-03 -6.112 9.87e-10 ***
## f.2443.0.0 -2.727e-02 2.987e-03 -9.129 < 2e-16 ***
## depr_l.0 -1.487e-01 2.884e-03 -51.561 < 2e-16 ***
## f.21001.0.0 -7.812e-02 3.062e-03 -25.514 < 2e-16 ***
## mean_hr.0 -3.740e-02 2.950e-03 -12.677 < 2e-16 ***
## f.738.0.0 1.167e-01 2.983e-03 39.121 < 2e-16 ***
## education.0 2.910e-02 2.868e-03 10.144 < 2e-16 ***
## ethn.0 6.593e-02 2.974e-03 22.168 < 2e-16 ***
## f.1200.0.0 -1.715e-01 2.900e-03 -59.140 < 2e-16 ***
## f.54.0.0 -4.132e-02 2.912e-03 -14.191 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.929 on 106111 degrees of freedom
## Multiple R-squared: 0.137, Adjusted R-squared: 0.1369
## F-statistic: 1053 on 16 and 106111 DF, p-value: < 2.2e-16car::vif(mdl_fit3_covs)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.296682 1.784806 1.941980 1.259425
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.095670 1.177631 1.164088 1.096978
## depr_l.0 f.21001.0.0 mean_hr.0 f.738.0.0
## 1.022673 1.152672 1.070288 1.093875
## education.0 ethn.0 f.1200.0.0 f.54.0.0
## 1.011713 1.087720 1.034231 1.042505# calculate delta adj. r squared
summary(mdl_fit3_covs)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.002845747Partcipants with hypertension diagnosis only:
# depressive mood
outcome <- c("depr_c.0")
predictors <- c("mean_sbp.0", "htn_meds_count.0") # without high_bp.0
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0", "f.21001.0.0", "mean_hr.0")
d <- subset(dat, high_bp.0 > 0)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(d, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5763 -0.5934 -0.2739 0.3661 5.0712
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.253e-15 2.842e-03 0.00 1
## f.21003.0.0 -1.784e-01 2.888e-03 -61.77 <2e-16 ***
## f.31.0.0 -5.130e-02 2.882e-03 -17.80 <2e-16 ***
## angina.0 8.343e-02 3.103e-03 26.89 <2e-16 ***
## heartattack.0 3.616e-02 3.095e-03 11.68 <2e-16 ***
## f.2443.0.0 4.388e-02 2.935e-03 14.95 <2e-16 ***
## depr_l.0 2.569e-01 2.860e-03 89.83 <2e-16 ***
## f.21001.0.0 7.722e-02 2.933e-03 26.33 <2e-16 ***
## mean_hr.0 4.372e-02 2.910e-03 15.03 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9306 on 107183 degrees of freedom
## Multiple R-squared: 0.1341, Adjusted R-squared: 0.1341
## F-statistic: 2075 on 8 and 107183 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit1_htn <- lm(mdl, data=dat_scaled)
summary(mdl_fit1_htn)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4971 -0.5922 -0.2707 0.3663 5.0641
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.209e-15 2.838e-03 0.000 1
## mean_sbp.0 -5.402e-02 2.939e-03 -18.381 < 2e-16 ***
## htn_meds_count.0 -1.536e-02 3.219e-03 -4.773 1.82e-06 ***
## f.21003.0.0 -1.661e-01 3.023e-03 -54.939 < 2e-16 ***
## f.31.0.0 -4.786e-02 2.884e-03 -16.595 < 2e-16 ***
## angina.0 8.297e-02 3.161e-03 26.248 < 2e-16 ***
## heartattack.0 3.478e-02 3.118e-03 11.153 < 2e-16 ***
## f.2443.0.0 4.225e-02 2.971e-03 14.221 < 2e-16 ***
## depr_l.0 2.541e-01 2.859e-03 88.884 < 2e-16 ***
## f.21001.0.0 7.895e-02 2.969e-03 26.590 < 2e-16 ***
## mean_hr.0 4.445e-02 2.913e-03 15.263 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9291 on 107181 degrees of freedom
## Multiple R-squared: 0.1369, Adjusted R-squared: 0.1368
## F-statistic: 1700 on 10 and 107181 DF, p-value: < 2.2e-16car::vif(mdl_fit1_htn)## mean_sbp.0 htn_meds_count.0 f.21003.0.0 f.31.0.0
## 1.072621 1.286750 1.134744 1.033030
## angina.0 heartattack.0 f.2443.0.0 depr_l.0
## 1.240689 1.207259 1.095909 1.015154
## f.21001.0.0 mean_hr.0
## 1.094622 1.053455# calculate delta adj. r squared
summary(mdl_fit1_htn)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.002740507# well-being
outcome <- c("wb.0")
d <- subset(dat, high_bp.0 > 0)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(d, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0,
f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.1229 -0.5807 0.0242 0.6263 4.1459
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.835e-15 4.449e-03 0.000 1.000
## f.21003.0.0 2.109e-01 4.513e-03 46.733 < 2e-16 ***
## f.31.0.0 -5.778e-03 4.503e-03 -1.283 0.199
## angina.0 -6.560e-02 4.823e-03 -13.603 < 2e-16 ***
## heartattack.0 -3.832e-02 4.817e-03 -7.955 1.83e-15 ***
## f.2443.0.0 -5.953e-02 4.588e-03 -12.975 < 2e-16 ***
## depr_l.0 -1.648e-01 4.475e-03 -36.823 < 2e-16 ***
## f.21001.0.0 -8.539e-02 4.580e-03 -18.642 < 2e-16 ***
## mean_hr.0 -4.016e-02 4.553e-03 -8.822 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9472 on 45310 degrees of freedom
## Multiple R-squared: 0.103, Adjusted R-squared: 0.1029
## F-statistic: 650.6 on 8 and 45310 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit3_htn <- lm(mdl, data=dat_scaled)
summary(mdl_fit3_htn)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0663 -0.5804 0.0242 0.6271 4.0929
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.849e-15 4.445e-03 0.000 1.0000
## mean_sbp.0 4.123e-02 4.594e-03 8.975 < 2e-16 ***
## htn_meds_count.0 1.010e-02 4.996e-03 2.023 0.0431 *
## f.21003.0.0 2.017e-01 4.720e-03 42.734 < 2e-16 ***
## f.31.0.0 -7.267e-03 4.504e-03 -1.613 0.1066
## angina.0 -6.488e-02 4.914e-03 -13.203 < 2e-16 ***
## heartattack.0 -3.691e-02 4.855e-03 -7.603 2.94e-14 ***
## f.2443.0.0 -5.797e-02 4.642e-03 -12.487 < 2e-16 ***
## depr_l.0 -1.630e-01 4.475e-03 -36.432 < 2e-16 ***
## f.21001.0.0 -8.663e-02 4.644e-03 -18.653 < 2e-16 ***
## mean_hr.0 -4.089e-02 4.558e-03 -8.971 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9463 on 45308 degrees of freedom
## Multiple R-squared: 0.1046, Adjusted R-squared: 0.1044
## F-statistic: 529.5 on 10 and 45308 DF, p-value: < 2.2e-16car::vif(mdl_fit3_htn)## mean_sbp.0 htn_meds_count.0 f.21003.0.0 f.31.0.0
## 1.067968 1.262875 1.127458 1.026405
## angina.0 heartattack.0 f.2443.0.0 depr_l.0
## 1.221964 1.192767 1.090496 1.013380
## f.21001.0.0 mean_hr.0
## 1.091420 1.051155# calculate delta adj. r squared
summary(mdl_fit3_htn)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.001566535Longitudinal regressions of Systolic Blood Pressure, Hypertension and Number of Antihypertensive Medications With Well-Being and Depressive Mood
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.2, phq9.0, wb.2, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0,
mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.2"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, depr_c.2, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0654 -0.6084 -0.2905 0.3144 6.2910
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.022e-16 5.697e-03 0.000 1.000000
## f.21003.0.0 -1.452e-01 5.777e-03 -25.137 < 2e-16 ***
## f.31.0.0 -6.443e-02 5.841e-03 -11.030 < 2e-16 ***
## angina.0 3.390e-02 5.978e-03 5.671 1.43e-08 ***
## heartattack.0 1.959e-02 5.964e-03 3.285 0.001020 **
## f.2443.0.0 2.021e-02 5.799e-03 3.484 0.000494 ***
## depr_l.0 2.250e-01 5.726e-03 39.290 < 2e-16 ***
## f.21001.0.0 7.145e-02 5.886e-03 12.138 < 2e-16 ***
## mean_hr.0 2.131e-02 5.866e-03 3.632 0.000281 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9536 on 28012 degrees of freedom
## Multiple R-squared: 0.09087, Adjusted R-squared: 0.09061
## F-statistic: 350 on 8 and 28012 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit4 <- lm(mdl, data=dat_scaled)
summary(mdl_fit4)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0569 -0.6103 -0.2881 0.3128 6.2804
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.757e-16 5.692e-03 0.000 1.000000
## mean_sbp.0 -4.223e-02 6.536e-03 -6.461 1.06e-10 ***
## high_bp.0 2.858e-02 7.624e-03 3.749 0.000178 ***
## htn_meds_count.0 -6.313e-03 7.798e-03 -0.810 0.418209
## f.21003.0.0 -1.363e-01 6.139e-03 -22.208 < 2e-16 ***
## f.31.0.0 -5.945e-02 5.929e-03 -10.027 < 2e-16 ***
## angina.0 3.263e-02 6.135e-03 5.319 1.05e-07 ***
## heartattack.0 1.830e-02 6.079e-03 3.011 0.002608 **
## f.2443.0.0 1.802e-02 5.852e-03 3.080 0.002073 **
## depr_l.0 2.238e-01 5.727e-03 39.084 < 2e-16 ***
## f.21001.0.0 7.404e-02 6.095e-03 12.148 < 2e-16 ***
## mean_hr.0 2.257e-02 5.888e-03 3.833 0.000127 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9529 on 28009 degrees of freedom
## Multiple R-squared: 0.09239, Adjusted R-squared: 0.09204
## F-statistic: 259.2 on 11 and 28009 DF, p-value: < 2.2e-16car::vif(mdl_fit4)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.318135 1.793580 1.876727 1.162943
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.084724 1.161448 1.140597 1.056760
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.012069 1.146360 1.069714outcome <- "phq9.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, phq9.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# calculate delta adj. r squared
summary(mdl_fit4)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.001427753# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2912 -0.5844 -0.2694 0.2990 6.5961
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.293e-15 2.973e-03 0.000 1
## f.21003.0.0 -1.590e-01 3.013e-03 -52.793 < 2e-16 ***
## f.31.0.0 -7.456e-02 3.044e-03 -24.494 < 2e-16 ***
## angina.0 4.074e-02 3.137e-03 12.988 < 2e-16 ***
## heartattack.0 1.441e-02 3.133e-03 4.597 4.28e-06 ***
## f.2443.0.0 2.014e-02 3.034e-03 6.640 3.16e-11 ***
## depr_l.0 1.981e-01 2.991e-03 66.245 < 2e-16 ***
## f.21001.0.0 1.260e-01 3.074e-03 40.978 < 2e-16 ***
## mean_hr.0 2.968e-02 3.052e-03 9.725 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9499 on 102081 degrees of freedom
## Multiple R-squared: 0.09775, Adjusted R-squared: 0.09768
## F-statistic: 1382 on 8 and 102081 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit5 <- lm(mdl, data=dat_scaled)
summary(mdl_fit5)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2963 -0.5836 -0.2668 0.3005 6.5874
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.351e-15 2.969e-03 0.000 1.000000
## mean_sbp.0 -4.906e-02 3.398e-03 -14.440 < 2e-16 ***
## high_bp.0 3.338e-02 3.989e-03 8.367 < 2e-16 ***
## htn_meds_count.0 -5.005e-03 4.134e-03 -1.211 0.226032
## f.21003.0.0 -1.490e-01 3.216e-03 -46.351 < 2e-16 ***
## f.31.0.0 -6.944e-02 3.081e-03 -22.536 < 2e-16 ***
## angina.0 3.884e-02 3.215e-03 12.080 < 2e-16 ***
## heartattack.0 1.245e-02 3.202e-03 3.889 0.000101 ***
## f.2443.0.0 1.712e-02 3.069e-03 5.578 2.44e-08 ***
## depr_l.0 1.967e-01 2.991e-03 65.784 < 2e-16 ***
## f.21001.0.0 1.285e-01 3.180e-03 40.413 < 2e-16 ***
## mean_hr.0 3.171e-02 3.065e-03 10.347 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9488 on 102078 degrees of freedom
## Multiple R-squared: 0.09988, Adjusted R-squared: 0.09978
## F-statistic: 1030 on 11 and 102078 DF, p-value: < 2.2e-16car::vif(mdl_fit5)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.309274 1.804663 1.937863 1.172561
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.076603 1.172513 1.162484 1.068389
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.014360 1.146448 1.065060# calculate delta adj. r squared
summary(mdl_fit5)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.002102968### well-being
outcome <- "wb.2"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, wb.2, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4249 -0.6318 0.0287 0.6286 3.4569
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.455e-16 5.619e-03 0.000 1.0000
## f.21003.0.0 1.573e-01 5.697e-03 27.604 < 2e-16 ***
## f.31.0.0 -7.129e-03 5.762e-03 -1.237 0.2161
## angina.0 -3.474e-02 5.913e-03 -5.875 4.26e-09 ***
## heartattack.0 -1.158e-02 5.901e-03 -1.962 0.0498 *
## f.2443.0.0 -2.854e-02 5.719e-03 -4.991 6.06e-07 ***
## depr_l.0 -1.347e-01 5.647e-03 -23.853 < 2e-16 ***
## f.21001.0.0 -7.274e-02 5.808e-03 -12.525 < 2e-16 ***
## mean_hr.0 -2.427e-02 5.786e-03 -4.194 2.75e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9727 on 29957 degrees of freedom
## Multiple R-squared: 0.05411, Adjusted R-squared: 0.05385
## F-statistic: 214.2 on 8 and 29957 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit6 <- lm(mdl, data=dat_scaled)
summary(mdl_fit6)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4182 -0.6316 0.0287 0.6275 3.4969
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.763e-16 5.615e-03 0.000 1.0000
## mean_sbp.0 3.288e-02 6.441e-03 5.105 3.33e-07 ***
## high_bp.0 -3.195e-02 7.518e-03 -4.250 2.14e-05 ***
## htn_meds_count.0 -3.817e-03 7.698e-03 -0.496 0.6200
## f.21003.0.0 1.534e-01 6.054e-03 25.337 < 2e-16 ***
## f.31.0.0 -9.866e-03 5.848e-03 -1.687 0.0916 .
## angina.0 -3.167e-02 6.075e-03 -5.213 1.87e-07 ***
## heartattack.0 -9.083e-03 6.009e-03 -1.511 0.1307
## f.2443.0.0 -2.507e-02 5.772e-03 -4.344 1.40e-05 ***
## depr_l.0 -1.340e-01 5.649e-03 -23.730 < 2e-16 ***
## f.21001.0.0 -7.117e-02 6.014e-03 -11.836 < 2e-16 ***
## mean_hr.0 -2.477e-02 5.808e-03 -4.265 2.00e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.972 on 29954 degrees of freedom
## Multiple R-squared: 0.05554, Adjusted R-squared: 0.05519
## F-statistic: 160.1 on 11 and 29954 DF, p-value: < 2.2e-16car::vif(mdl_fit6)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.315928 1.792629 1.879275 1.162289
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.084812 1.170576 1.145324 1.056598
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.011970 1.146927 1.069926# calculate delta adj. r squared
summary(mdl_fit6)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.001336895Sensitivity analyses
HTN medication effects
# HTN meds
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, htnmeds_ctgry.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- "htnmeds_ctgry.0"
covs <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0","f.21003.0.0", "f.31.0.0", "angina.0",
"heartattack.0", "f.2443.0.0", "depr_l.0", "f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
mdl <- as.formula(paste(paste(outcome), paste(predictors, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -0.6808, -0.40244, -0.18057, 0.28654, 2.67797
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.4538043 0.0579872 25.071
## htnmeds_ctgry.0ace inhibitor -0.0513693 0.0581445 -0.883
## htnmeds_ctgry.0ace inhibitor + diuretic 0.0241368 0.1116293 0.216
## htnmeds_ctgry.0ace inhibitor + thiazid -0.1317704 0.0714862 -1.843
## htnmeds_ctgry.0aldosteron antagonist 0.1489734 0.0824607 1.807
## htnmeds_ctgry.0alpha1 blocker -0.0219116 0.0594285 -0.369
## htnmeds_ctgry.0alpha2 agonist 0.1141568 0.0621538 1.837
## htnmeds_ctgry.0at1 blocker -0.0339360 0.0583408 -0.582
## htnmeds_ctgry.0at1 blocker + thiazid -0.0329306 0.0629540 -0.523
## htnmeds_ctgry.0beta and alpha1 blocker -0.0788043 0.0953481 -0.826
## htnmeds_ctgry.0beta blocker 0.0096569 0.0580999 0.166
## htnmeds_ctgry.0beta blocker + thiazid 0.0150455 0.0659611 0.228
## htnmeds_ctgry.0calcium antagonist -0.0232343 0.0580903 -0.400
## htnmeds_ctgry.0calcium antagonist + ace inhibitor -0.2332161 0.1468322 -1.588
## htnmeds_ctgry.0diuretic -0.0155355 0.0602856 -0.258
## htnmeds_ctgry.0loop diuretic 0.2028040 0.0595753 3.404
## htnmeds_ctgry.0loop diuretic + diuretic 0.0177866 0.1019473 0.174
## htnmeds_ctgry.0nitrate 0.2269992 0.0603219 3.763
## htnmeds_ctgry.0no agonist -0.0430901 0.1200450 -0.359
## htnmeds_ctgry.0phosphodiesterase3 inhibitor -0.0006793 0.1506553 -0.005
## htnmeds_ctgry.0phosphodiesterase5 inhibitor -0.0319686 0.0601816 -0.531
## htnmeds_ctgry.0synthetic amino acid 0.0640528 0.1595590 0.401
## htnmeds_ctgry.0thiazide -0.0618593 0.0581317 -1.064
## htnmeds_ctgry.0thiazide + other diuretic -0.0022278 0.0689637 -0.032
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## htnmeds_ctgry.0ace inhibitor 0.376981
## htnmeds_ctgry.0ace inhibitor + diuretic 0.828814
## htnmeds_ctgry.0ace inhibitor + thiazid 0.065288 .
## htnmeds_ctgry.0aldosteron antagonist 0.070828 .
## htnmeds_ctgry.0alpha1 blocker 0.712349
## htnmeds_ctgry.0alpha2 agonist 0.066259 .
## htnmeds_ctgry.0at1 blocker 0.560780
## htnmeds_ctgry.0at1 blocker + thiazid 0.600913
## htnmeds_ctgry.0beta and alpha1 blocker 0.408528
## htnmeds_ctgry.0beta blocker 0.867990
## htnmeds_ctgry.0beta blocker + thiazid 0.819572
## htnmeds_ctgry.0calcium antagonist 0.689181
## htnmeds_ctgry.0calcium antagonist + ace inhibitor 0.112218
## htnmeds_ctgry.0diuretic 0.796641
## htnmeds_ctgry.0loop diuretic 0.000664 ***
## htnmeds_ctgry.0loop diuretic + diuretic 0.861498
## htnmeds_ctgry.0nitrate 0.000168 ***
## htnmeds_ctgry.0no agonist 0.719634
## htnmeds_ctgry.0phosphodiesterase3 inhibitor 0.996402
## htnmeds_ctgry.0phosphodiesterase5 inhibitor 0.595279
## htnmeds_ctgry.0synthetic amino acid 0.688100
## htnmeds_ctgry.0thiazide 0.287275
## htnmeds_ctgry.0thiazide + other diuretic 0.974230
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5562 on 101560 degrees of freedom
## (400910 observations deleted due to missingness)
## Multiple R-squared: 0.007579, Adjusted R-squared: 0.007355
## F-statistic: 33.72 on 23 and 101560 DF, p-value: < 2.2e-16# with covariates
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit7 <- lm(mdl, data=dat)
summary(mdl_fit7)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -1.4616, -0.3264, -0.15088, 0.20093, 2.86178
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 1.967e+00 6.415e-02 30.665
## htnmeds_ctgry.0ace inhibitor -6.654e-02 5.793e-02 -1.148
## htnmeds_ctgry.0ace inhibitor + diuretic 2.717e-02 1.088e-01 0.250
## htnmeds_ctgry.0ace inhibitor + thiazid -1.096e-01 7.027e-02 -1.560
## htnmeds_ctgry.0aldosteron antagonist 6.853e-02 8.287e-02 0.827
## htnmeds_ctgry.0alpha1 blocker -2.389e-02 5.917e-02 -0.404
## htnmeds_ctgry.0alpha2 agonist 2.392e-02 6.231e-02 0.384
## htnmeds_ctgry.0at1 blocker -4.773e-02 5.812e-02 -0.821
## htnmeds_ctgry.0at1 blocker + thiazid -4.737e-02 6.247e-02 -0.758
## htnmeds_ctgry.0beta and alpha1 blocker -1.036e-01 9.401e-02 -1.102
## htnmeds_ctgry.0beta blocker -1.197e-02 5.795e-02 -0.207
## htnmeds_ctgry.0beta blocker + thiazid -4.978e-04 6.551e-02 -0.008
## htnmeds_ctgry.0calcium antagonist -3.490e-02 5.792e-02 -0.603
## htnmeds_ctgry.0calcium antagonist + ace inhibitor -2.158e-01 1.446e-01 -1.492
## htnmeds_ctgry.0diuretic -3.919e-02 5.996e-02 -0.654
## htnmeds_ctgry.0loop diuretic 1.005e-01 5.946e-02 1.690
## htnmeds_ctgry.0loop diuretic + diuretic -7.705e-02 9.972e-02 -0.773
## htnmeds_ctgry.0nitrate 8.222e-02 6.031e-02 1.363
## htnmeds_ctgry.0no agonist -8.116e-02 1.218e-01 -0.667
## htnmeds_ctgry.0phosphodiesterase3 inhibitor -2.839e-03 1.447e-01 -0.020
## htnmeds_ctgry.0phosphodiesterase5 inhibitor 4.021e-02 6.036e-02 0.666
## htnmeds_ctgry.0synthetic amino acid -9.589e-02 1.592e-01 -0.602
## htnmeds_ctgry.0thiazide -6.815e-02 5.796e-02 -1.176
## htnmeds_ctgry.0thiazide + other diuretic -4.798e-02 6.873e-02 -0.698
## mean_sbp.0 -1.441e-03 9.958e-05 -14.474
## high_bp.0 9.556e-03 5.274e-03 1.812
## htn_meds_count.0 8.303e-03 2.228e-03 3.726
## f.21003.0.0 -1.408e-02 2.768e-04 -50.846
## f.31.0.0 -6.847e-02 3.586e-03 -19.093
## angina.0 1.461e-01 6.031e-03 24.216
## heartattack.0 4.412e-02 6.749e-03 6.537
## f.2443.0.0 6.270e-02 4.863e-03 12.892
## depr_l.0 5.874e-01 7.114e-03 82.577
## f.21001.0.0 8.036e-03 3.441e-04 23.351
## mean_hr.0 2.202e-03 1.469e-04 14.994
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## htnmeds_ctgry.0ace inhibitor 0.250770
## htnmeds_ctgry.0ace inhibitor + diuretic 0.802845
## htnmeds_ctgry.0ace inhibitor + thiazid 0.118694
## htnmeds_ctgry.0aldosteron antagonist 0.408257
## htnmeds_ctgry.0alpha1 blocker 0.686376
## htnmeds_ctgry.0alpha2 agonist 0.701031
## htnmeds_ctgry.0at1 blocker 0.411527
## htnmeds_ctgry.0at1 blocker + thiazid 0.448258
## htnmeds_ctgry.0beta and alpha1 blocker 0.270263
## htnmeds_ctgry.0beta blocker 0.836385
## htnmeds_ctgry.0beta blocker + thiazid 0.993937
## htnmeds_ctgry.0calcium antagonist 0.546824
## htnmeds_ctgry.0calcium antagonist + ace inhibitor 0.135708
## htnmeds_ctgry.0diuretic 0.513392
## htnmeds_ctgry.0loop diuretic 0.090992 .
## htnmeds_ctgry.0loop diuretic + diuretic 0.439765
## htnmeds_ctgry.0nitrate 0.172836
## htnmeds_ctgry.0no agonist 0.505031
## htnmeds_ctgry.0phosphodiesterase3 inhibitor 0.984344
## htnmeds_ctgry.0phosphodiesterase5 inhibitor 0.505278
## htnmeds_ctgry.0synthetic amino acid 0.546846
## htnmeds_ctgry.0thiazide 0.239685
## htnmeds_ctgry.0thiazide + other diuretic 0.485099
## mean_sbp.0 < 2e-16 ***
## high_bp.0 0.069980 .
## htn_meds_count.0 0.000195 ***
## f.21003.0.0 < 2e-16 ***
## f.31.0.0 < 2e-16 ***
## angina.0 < 2e-16 ***
## heartattack.0 6.31e-11 ***
## f.2443.0.0 < 2e-16 ***
## depr_l.0 < 2e-16 ***
## f.21001.0.0 < 2e-16 ***
## mean_hr.0 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5135 on 90965 degrees of freedom
## (411494 observations deleted due to missingness)
## Multiple R-squared: 0.1438, Adjusted R-squared: 0.1434
## F-statistic: 449.2 on 34 and 90965 DF, p-value: < 2.2e-16# calculate delta adj. r squared
summary(mdl_fit7)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.1360839### well-being
outcome <- "wb.0"
mdl <- as.formula(paste(paste(outcome), paste(predictors, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.4768, -0.3989, 0.0232, 0.3967, 1.7281
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.416667 0.076354 57.845
## htnmeds_ctgry.0ace inhibitor 0.019977 0.076694 0.260
## htnmeds_ctgry.0ace inhibitor + diuretic -0.275641 0.180934 -1.523
## htnmeds_ctgry.0ace inhibitor + thiazid 0.098387 0.107106 0.919
## htnmeds_ctgry.0aldosteron antagonist -0.283824 0.126957 -2.236
## htnmeds_ctgry.0alpha1 blocker 0.002589 0.079670 0.032
## htnmeds_ctgry.0alpha2 agonist 0.003841 0.084831 0.045
## htnmeds_ctgry.0at1 blocker 0.023021 0.077057 0.299
## htnmeds_ctgry.0at1 blocker + thiazid 0.013696 0.086957 0.158
## htnmeds_ctgry.0beta and alpha1 blocker 0.205556 0.170732 1.204
## htnmeds_ctgry.0beta blocker -0.017804 0.076613 -0.232
## htnmeds_ctgry.0beta blocker + thiazid -0.017192 0.092651 -0.186
## htnmeds_ctgry.0calcium antagonist -0.007464 0.076555 -0.098
## htnmeds_ctgry.0calcium antagonist + ace inhibitor 0.205556 0.211414 0.972
## htnmeds_ctgry.0diuretic -0.005034 0.080537 -0.063
## htnmeds_ctgry.0loop diuretic -0.156640 0.079880 -1.961
## htnmeds_ctgry.0loop diuretic + diuretic -0.100000 0.175543 -0.570
## htnmeds_ctgry.0nitrate -0.144761 0.081828 -1.769
## htnmeds_ctgry.0no agonist -0.032051 0.180934 -0.177
## htnmeds_ctgry.0phosphodiesterase3 inhibitor 0.333333 0.305416 1.091
## htnmeds_ctgry.0phosphodiesterase5 inhibitor 0.007546 0.081712 0.092
## htnmeds_ctgry.0synthetic amino acid -0.035714 0.236222 -0.151
## htnmeds_ctgry.0thiazide 0.060140 0.076692 0.784
## htnmeds_ctgry.0thiazide + other diuretic 0.137597 0.099485 1.383
## Pr(>|t|)
## (Intercept) <2e-16 ***
## htnmeds_ctgry.0ace inhibitor 0.7945
## htnmeds_ctgry.0ace inhibitor + diuretic 0.1277
## htnmeds_ctgry.0ace inhibitor + thiazid 0.3583
## htnmeds_ctgry.0aldosteron antagonist 0.0254 *
## htnmeds_ctgry.0alpha1 blocker 0.9741
## htnmeds_ctgry.0alpha2 agonist 0.9639
## htnmeds_ctgry.0at1 blocker 0.7651
## htnmeds_ctgry.0at1 blocker + thiazid 0.8748
## htnmeds_ctgry.0beta and alpha1 blocker 0.2286
## htnmeds_ctgry.0beta blocker 0.8162
## htnmeds_ctgry.0beta blocker + thiazid 0.8528
## htnmeds_ctgry.0calcium antagonist 0.9223
## htnmeds_ctgry.0calcium antagonist + ace inhibitor 0.3309
## htnmeds_ctgry.0diuretic 0.9502
## htnmeds_ctgry.0loop diuretic 0.0499 *
## htnmeds_ctgry.0loop diuretic + diuretic 0.5689
## htnmeds_ctgry.0nitrate 0.0769 .
## htnmeds_ctgry.0no agonist 0.8594
## htnmeds_ctgry.0phosphodiesterase3 inhibitor 0.2751
## htnmeds_ctgry.0phosphodiesterase5 inhibitor 0.9264
## htnmeds_ctgry.0synthetic amino acid 0.8798
## htnmeds_ctgry.0thiazide 0.4329
## htnmeds_ctgry.0thiazide + other diuretic 0.1666
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5914 on 40496 degrees of freedom
## (461974 observations deleted due to missingness)
## Multiple R-squared: 0.004412, Adjusted R-squared: 0.003846
## F-statistic: 7.802 on 23 and 40496 DF, p-value: < 2.2e-16# with covariates
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit8 <- lm(mdl, data=dat)
summary(mdl_fit8)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.6632, -0.3489, 0.0103, 0.3718, 2.463
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.8691231 0.0856994 45.148
## htnmeds_ctgry.0ace inhibitor 0.0488877 0.0727678 0.672
## htnmeds_ctgry.0ace inhibitor + diuretic -0.1968809 0.1716067 -1.147
## htnmeds_ctgry.0ace inhibitor + thiazid 0.1211412 0.1019975 1.188
## htnmeds_ctgry.0aldosteron antagonist -0.2610805 0.1229683 -2.123
## htnmeds_ctgry.0alpha1 blocker 0.0179303 0.0757409 0.237
## htnmeds_ctgry.0alpha2 agonist 0.0645949 0.0817265 0.790
## htnmeds_ctgry.0at1 blocker 0.0393325 0.0731283 0.538
## htnmeds_ctgry.0at1 blocker + thiazid 0.0232985 0.0826839 0.282
## htnmeds_ctgry.0beta and alpha1 blocker 0.3469247 0.1619744 2.142
## htnmeds_ctgry.0beta blocker 0.0017737 0.0728185 0.024
## htnmeds_ctgry.0beta blocker + thiazid -0.0434887 0.0883449 -0.492
## htnmeds_ctgry.0calcium antagonist 0.0117951 0.0727031 0.162
## htnmeds_ctgry.0calcium antagonist + ace inhibitor 0.1841910 0.2111343 0.872
## htnmeds_ctgry.0diuretic 0.0019490 0.0766632 0.025
## htnmeds_ctgry.0loop diuretic -0.0673557 0.0761701 -0.884
## htnmeds_ctgry.0loop diuretic + diuretic -0.1223966 0.1666248 -0.735
## htnmeds_ctgry.0nitrate -0.0406848 0.0782551 -0.520
## htnmeds_ctgry.0no agonist -0.0345601 0.1917043 -0.180
## htnmeds_ctgry.0phosphodiesterase3 inhibitor 0.2451544 0.2897782 0.846
## htnmeds_ctgry.0phosphodiesterase5 inhibitor -0.0214974 0.0784902 -0.274
## htnmeds_ctgry.0synthetic amino acid 0.0027391 0.2402815 0.011
## htnmeds_ctgry.0thiazide 0.0579033 0.0728376 0.795
## htnmeds_ctgry.0thiazide + other diuretic 0.1290551 0.0959871 1.345
## mean_sbp.0 0.0014677 0.0001657 8.856
## high_bp.0 -0.0281169 0.0087278 -3.222
## htn_meds_count.0 -0.0104345 0.0037458 -2.786
## f.21003.0.0 0.0176105 0.0004569 38.542
## f.31.0.0 0.0079283 0.0059447 1.334
## angina.0 -0.1246237 0.0101435 -12.286
## heartattack.0 -0.0674416 0.0113669 -5.933
## f.2443.0.0 -0.0952319 0.0077403 -12.303
## depr_l.0 -0.3950629 0.0120222 -32.861
## f.21001.0.0 -0.0081529 0.0005661 -14.402
## mean_hr.0 -0.0019949 0.0002447 -8.151
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## htnmeds_ctgry.0ace inhibitor 0.50170
## htnmeds_ctgry.0ace inhibitor + diuretic 0.25127
## htnmeds_ctgry.0ace inhibitor + thiazid 0.23496
## htnmeds_ctgry.0aldosteron antagonist 0.03375 *
## htnmeds_ctgry.0alpha1 blocker 0.81287
## htnmeds_ctgry.0alpha2 agonist 0.42931
## htnmeds_ctgry.0at1 blocker 0.59068
## htnmeds_ctgry.0at1 blocker + thiazid 0.77811
## htnmeds_ctgry.0beta and alpha1 blocker 0.03221 *
## htnmeds_ctgry.0beta blocker 0.98057
## htnmeds_ctgry.0beta blocker + thiazid 0.62254
## htnmeds_ctgry.0calcium antagonist 0.87112
## htnmeds_ctgry.0calcium antagonist + ace inhibitor 0.38300
## htnmeds_ctgry.0diuretic 0.97972
## htnmeds_ctgry.0loop diuretic 0.37655
## htnmeds_ctgry.0loop diuretic + diuretic 0.46261
## htnmeds_ctgry.0nitrate 0.60314
## htnmeds_ctgry.0no agonist 0.85693
## htnmeds_ctgry.0phosphodiesterase3 inhibitor 0.39755
## htnmeds_ctgry.0phosphodiesterase5 inhibitor 0.78417
## htnmeds_ctgry.0synthetic amino acid 0.99090
## htnmeds_ctgry.0thiazide 0.42664
## htnmeds_ctgry.0thiazide + other diuretic 0.17879
## mean_sbp.0 < 2e-16 ***
## high_bp.0 0.00128 **
## htn_meds_count.0 0.00535 **
## f.21003.0.0 < 2e-16 ***
## f.31.0.0 0.18232
## angina.0 < 2e-16 ***
## heartattack.0 3.00e-09 ***
## f.2443.0.0 < 2e-16 ***
## depr_l.0 < 2e-16 ***
## f.21001.0.0 < 2e-16 ***
## mean_hr.0 3.71e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5609 on 39190 degrees of freedom
## (463269 observations deleted due to missingness)
## Multiple R-squared: 0.1019, Adjusted R-squared: 0.1012
## F-statistic: 130.8 on 34 and 39190 DF, p-value: < 2.2e-16# calculate delta adj. r squared
summary(mdl_fit8)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.09732096DEPR medication effects
# DEPR meds
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, deprmeds_ctgry.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- "deprmeds_ctgry.0"
covs <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0","f.21003.0.0", "f.31.0.0", "angina.0",
"heartattack.0", "f.2443.0.0", "depr_l.0", "f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
mdl <- as.formula(paste(paste(outcome), paste(predictors, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -1.20588, -0.55289, -0.09178, 0.44711, 2.3169
##
## Coefficients:
## Estimate
## (Intercept) 2.2059
## deprmeds_ctgry.0selective serotonin reuptake inhibitor -0.2265
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor -0.1141
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor -0.1026
## deprmeds_ctgry.0tricyclic antidepressant -0.4030
## deprmeds_ctgry.0unselective reuptake inhibitor -0.5228
## Std. Error
## (Intercept) 0.1321
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.1322
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.1335
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.1352
## deprmeds_ctgry.0tricyclic antidepressant 0.1323
## deprmeds_ctgry.0unselective reuptake inhibitor 0.1349
## t value
## (Intercept) 16.703
## deprmeds_ctgry.0selective serotonin reuptake inhibitor -1.713
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor -0.855
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor -0.759
## deprmeds_ctgry.0tricyclic antidepressant -3.047
## deprmeds_ctgry.0unselective reuptake inhibitor -3.877
## Pr(>|t|)
## (Intercept) < 2e-16
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.086688
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.392742
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.448062
## deprmeds_ctgry.0tricyclic antidepressant 0.002312
## deprmeds_ctgry.0unselective reuptake inhibitor 0.000106
##
## (Intercept) ***
## deprmeds_ctgry.0selective serotonin reuptake inhibitor .
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor
## deprmeds_ctgry.0tricyclic antidepressant **
## deprmeds_ctgry.0unselective reuptake inhibitor ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7701 on 33127 degrees of freedom
## (469361 observations deleted due to missingness)
## Multiple R-squared: 0.01732, Adjusted R-squared: 0.01717
## F-statistic: 116.8 on 5 and 33127 DF, p-value: < 2.2e-16# with covariates
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit77 <- lm(mdl, data=dat)
summary(mdl_fit77)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -1.74081, -0.53961, -0.13388, 0.37981, 2.63593
##
## Coefficients:
## Estimate
## (Intercept) 2.4197602
## deprmeds_ctgry.0selective serotonin reuptake inhibitor -0.2497775
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor -0.1756552
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor -0.1339910
## deprmeds_ctgry.0tricyclic antidepressant -0.2956308
## deprmeds_ctgry.0unselective reuptake inhibitor -0.3538236
## mean_sbp.0 -0.0017665
## high_bp.0 0.0712447
## htn_meds_count.0 -0.0053679
## f.21003.0.0 -0.0193909
## f.31.0.0 0.0791340
## angina.0 0.2286350
## heartattack.0 0.1205360
## f.2443.0.0 0.0514082
## depr_l.0 0.2758695
## f.21001.0.0 0.0122005
## mean_hr.0 0.0040841
## Std. Error
## (Intercept) 0.1511742
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.1340563
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.1352802
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.1371561
## deprmeds_ctgry.0tricyclic antidepressant 0.1340936
## deprmeds_ctgry.0unselective reuptake inhibitor 0.1375834
## mean_sbp.0 0.0002701
## high_bp.0 0.0118671
## htn_meds_count.0 0.0061421
## f.21003.0.0 0.0006100
## f.31.0.0 0.0096147
## angina.0 0.0222047
## heartattack.0 0.0280827
## f.2443.0.0 0.0160040
## depr_l.0 0.0095736
## f.21001.0.0 0.0008279
## mean_hr.0 0.0003867
## t value
## (Intercept) 16.006
## deprmeds_ctgry.0selective serotonin reuptake inhibitor -1.863
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor -1.298
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor -0.977
## deprmeds_ctgry.0tricyclic antidepressant -2.205
## deprmeds_ctgry.0unselective reuptake inhibitor -2.572
## mean_sbp.0 -6.540
## high_bp.0 6.004
## htn_meds_count.0 -0.874
## f.21003.0.0 -31.788
## f.31.0.0 8.231
## angina.0 10.297
## heartattack.0 4.292
## f.2443.0.0 3.212
## depr_l.0 28.816
## f.21001.0.0 14.737
## mean_hr.0 10.561
## Pr(>|t|)
## (Intercept) < 2e-16
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.06244
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.19414
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.32862
## deprmeds_ctgry.0tricyclic antidepressant 0.02749
## deprmeds_ctgry.0unselective reuptake inhibitor 0.01012
## mean_sbp.0 6.24e-11
## high_bp.0 1.95e-09
## htn_meds_count.0 0.38215
## f.21003.0.0 < 2e-16
## f.31.0.0 < 2e-16
## angina.0 < 2e-16
## heartattack.0 1.78e-05
## f.2443.0.0 0.00132
## depr_l.0 < 2e-16
## f.21001.0.0 < 2e-16
## mean_hr.0 < 2e-16
##
## (Intercept) ***
## deprmeds_ctgry.0selective serotonin reuptake inhibitor .
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor
## deprmeds_ctgry.0tricyclic antidepressant *
## deprmeds_ctgry.0unselective reuptake inhibitor *
## mean_sbp.0 ***
## high_bp.0 ***
## htn_meds_count.0
## f.21003.0.0 ***
## f.31.0.0 ***
## angina.0 ***
## heartattack.0 ***
## f.2443.0.0 **
## depr_l.0 ***
## f.21001.0.0 ***
## mean_hr.0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7328 on 28705 degrees of freedom
## (473772 observations deleted due to missingness)
## Multiple R-squared: 0.1126, Adjusted R-squared: 0.1121
## F-statistic: 227.7 on 16 and 28705 DF, p-value: < 2.2e-16# calculate delta adj. r squared
summary(mdl_fit77)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.09498038### well-being
outcome <- "wb.0"
mdl <- as.formula(paste(paste(outcome), paste(predictors, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.1674, -0.3582, 0.0584, 0.4918, 2.0504
##
## Coefficients:
## Estimate
## (Intercept) 3.91111
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.19713
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.03853
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.07441
## deprmeds_ctgry.0tricyclic antidepressant 0.25628
## deprmeds_ctgry.0unselective reuptake inhibitor 0.30351
## Std. Error
## (Intercept) 0.20102
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.20119
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.20303
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.20641
## deprmeds_ctgry.0tricyclic antidepressant 0.20129
## deprmeds_ctgry.0unselective reuptake inhibitor 0.20796
## t value
## (Intercept) 19.456
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.980
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.190
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.360
## deprmeds_ctgry.0tricyclic antidepressant 1.273
## deprmeds_ctgry.0unselective reuptake inhibitor 1.459
## Pr(>|t|)
## (Intercept) <2e-16
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.327
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.850
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.718
## deprmeds_ctgry.0tricyclic antidepressant 0.203
## deprmeds_ctgry.0unselective reuptake inhibitor 0.144
##
## (Intercept) ***
## deprmeds_ctgry.0selective serotonin reuptake inhibitor
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor
## deprmeds_ctgry.0tricyclic antidepressant
## deprmeds_ctgry.0unselective reuptake inhibitor
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6964 on 12659 degrees of freedom
## (489829 observations deleted due to missingness)
## Multiple R-squared: 0.005604, Adjusted R-squared: 0.005211
## F-statistic: 14.27 on 5 and 12659 DF, p-value: 5.924e-14# with covariates
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit88 <- lm(mdl, data=dat)
summary(mdl_fit88)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.09315, -0.35872, 0.03709, 0.4243, 2.4928
##
## Coefficients:
## Estimate
## (Intercept) 3.8272026
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.0583582
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor -0.0577296
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor -0.0602160
## deprmeds_ctgry.0tricyclic antidepressant 0.0104187
## deprmeds_ctgry.0unselective reuptake inhibitor 0.0011186
## mean_sbp.0 0.0016230
## high_bp.0 -0.1002397
## htn_meds_count.0 0.0147676
## f.21003.0.0 0.0212296
## f.31.0.0 -0.1476886
## angina.0 -0.1504044
## heartattack.0 -0.1203933
## f.2443.0.0 -0.0911503
## depr_l.0 -0.2079113
## f.21001.0.0 -0.0117809
## mean_hr.0 -0.0027531
## Std. Error
## (Intercept) 0.2117789
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.1890666
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.1906906
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.1942422
## deprmeds_ctgry.0tricyclic antidepressant 0.1891095
## deprmeds_ctgry.0unselective reuptake inhibitor 0.1971063
## mean_sbp.0 0.0003755
## high_bp.0 0.0165058
## htn_meds_count.0 0.0084738
## f.21003.0.0 0.0008290
## f.31.0.0 0.0132060
## angina.0 0.0309565
## heartattack.0 0.0392492
## f.2443.0.0 0.0212222
## depr_l.0 0.0132442
## f.21001.0.0 0.0011412
## mean_hr.0 0.0005439
## t value
## (Intercept) 18.072
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.309
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor -0.303
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor -0.310
## deprmeds_ctgry.0tricyclic antidepressant 0.055
## deprmeds_ctgry.0unselective reuptake inhibitor 0.006
## mean_sbp.0 4.322
## high_bp.0 -6.073
## htn_meds_count.0 1.743
## f.21003.0.0 25.608
## f.31.0.0 -11.183
## angina.0 -4.859
## heartattack.0 -3.067
## f.2443.0.0 -4.295
## depr_l.0 -15.698
## f.21001.0.0 -10.323
## mean_hr.0 -5.061
## Pr(>|t|)
## (Intercept) < 2e-16
## deprmeds_ctgry.0selective serotonin reuptake inhibitor 0.75758
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor 0.76209
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor 0.75656
## deprmeds_ctgry.0tricyclic antidepressant 0.95606
## deprmeds_ctgry.0unselective reuptake inhibitor 0.99547
## mean_sbp.0 1.56e-05
## high_bp.0 1.29e-09
## htn_meds_count.0 0.08140
## f.21003.0.0 < 2e-16
## f.31.0.0 < 2e-16
## angina.0 1.20e-06
## heartattack.0 0.00216
## f.2443.0.0 1.76e-05
## depr_l.0 < 2e-16
## f.21001.0.0 < 2e-16
## mean_hr.0 4.23e-07
##
## (Intercept) ***
## deprmeds_ctgry.0selective serotonin reuptake inhibitor
## deprmeds_ctgry.0selective serotonin-noradrelin reuptake inhibitor
## deprmeds_ctgry.0selective serotonin-noradrenalin reuptake inhibitor
## deprmeds_ctgry.0tricyclic antidepressant
## deprmeds_ctgry.0unselective reuptake inhibitor
## mean_sbp.0 ***
## high_bp.0 ***
## htn_meds_count.0 .
## f.21003.0.0 ***
## f.31.0.0 ***
## angina.0 ***
## heartattack.0 **
## f.2443.0.0 ***
## depr_l.0 ***
## f.21001.0.0 ***
## mean_hr.0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6534 on 12029 degrees of freedom
## (490448 observations deleted due to missingness)
## Multiple R-squared: 0.1224, Adjusted R-squared: 0.1212
## F-statistic: 104.8 on 16 and 12029 DF, p-value: < 2.2e-16# calculate delta adj. r squared
summary(mdl_fit88)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.1160088partcipants without/with depression meds only
### partcipants with/without depression meds only
# prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
depr_meds_count.0, f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0") # without depr_meds_count.0
# depressive mood
outcome <- "depr_c.0"
dat_filtered <- filter(dat, depr_meds_count.0 < 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0))))
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_depr_meds0 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_depr_meds0)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2075 -0.6333 -0.2986 0.3806 5.7450
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.296e-14 1.844e-03 0.000 1
## mean_sbp.0 -6.412e-02 2.082e-03 -30.800 < 2e-16 ***
## high_bp.0 4.642e-02 2.427e-03 19.130 < 2e-16 ***
## htn_meds_count.0 -1.303e-02 2.573e-03 -5.063 4.13e-07 ***
## f.21003.0.0 -1.599e-01 2.005e-03 -79.728 < 2e-16 ***
## f.31.0.0 -5.285e-02 1.895e-03 -27.890 < 2e-16 ***
## angina.0 6.130e-02 2.034e-03 30.137 < 2e-16 ***
## heartattack.0 2.167e-02 2.019e-03 10.735 < 2e-16 ***
## f.2443.0.0 2.723e-02 1.923e-03 14.159 < 2e-16 ***
## depr_l.0 1.282e-01 1.847e-03 69.395 < 2e-16 ***
## f.21001.0.0 6.571e-02 1.968e-03 33.395 < 2e-16 ***
## mean_hr.0 4.330e-02 1.899e-03 22.798 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9668 on 275037 degrees of freedom
## Multiple R-squared: 0.06524, Adjusted R-squared: 0.0652
## F-statistic: 1745 on 11 and 275037 DF, p-value: < 2.2e-16dat_filtered <- filter(dat, depr_meds_count.0 > 0)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0, angina.0,
heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0,
mean_hr.0))))
mdl_fit_depr_meds1 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_depr_meds1)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1371 -0.6935 -0.1740 0.4905 3.3668
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.643e-16 5.566e-03 0.000 1.000000
## mean_sbp.0 -4.249e-02 6.207e-03 -6.845 7.79e-12 ***
## high_bp.0 4.457e-02 7.295e-03 6.109 1.02e-09 ***
## htn_meds_count.0 -9.170e-03 8.057e-03 -1.138 0.255051
## f.21003.0.0 -1.967e-01 6.091e-03 -32.289 < 2e-16 ***
## f.31.0.0 4.875e-02 5.749e-03 8.480 < 2e-16 ***
## angina.0 6.673e-02 6.499e-03 10.267 < 2e-16 ***
## heartattack.0 2.751e-02 6.246e-03 4.404 1.07e-05 ***
## f.2443.0.0 1.981e-02 5.936e-03 3.336 0.000851 ***
## depr_l.0 1.907e-01 5.705e-03 33.425 < 2e-16 ***
## f.21001.0.0 9.097e-02 6.000e-03 15.161 < 2e-16 ***
## mean_hr.0 5.952e-02 5.753e-03 10.345 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9433 on 28710 degrees of freedom
## Multiple R-squared: 0.1105, Adjusted R-squared: 0.1102
## F-statistic: 324.4 on 11 and 28710 DF, p-value: < 2.2e-16# well-being
outcome <- "wb.0"
dat_filtered <- filter(dat, depr_meds_count.0 < 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, wb.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0))))
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_wb_depr_meds0 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_wb_depr_meds0)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.6123 -0.6162 0.0187 0.6446 3.7564
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.838e-15 2.826e-03 0.000 1.000
## mean_sbp.0 5.614e-02 3.184e-03 17.632 < 2e-16 ***
## high_bp.0 -5.710e-02 3.738e-03 -15.274 < 2e-16 ***
## htn_meds_count.0 9.345e-03 3.951e-03 2.365 0.018 *
## f.21003.0.0 1.751e-01 3.072e-03 57.016 < 2e-16 ***
## f.31.0.0 -2.523e-02 2.896e-03 -8.712 < 2e-16 ***
## angina.0 -4.844e-02 3.091e-03 -15.671 < 2e-16 ***
## heartattack.0 -2.329e-02 3.071e-03 -7.583 3.4e-14 ***
## f.2443.0.0 -3.684e-02 2.944e-03 -12.512 < 2e-16 ***
## depr_l.0 -8.351e-02 2.830e-03 -29.508 < 2e-16 ***
## f.21001.0.0 -7.716e-02 3.016e-03 -25.588 < 2e-16 ***
## mean_hr.0 -4.283e-02 2.913e-03 -14.702 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9701 on 117818 degrees of freedom
## Multiple R-squared: 0.05901, Adjusted R-squared: 0.05892
## F-statistic: 671.6 on 11 and 117818 DF, p-value: < 2.2e-16dat_filtered <- filter(dat, depr_meds_count.0 > 0)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, wb.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0, angina.0,
heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0,
mean_hr.0))))
mdl_fit_wb_depr_meds1 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_wb_depr_meds1)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4415 -0.5120 0.0512 0.6110 3.6211
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.368e-17 8.549e-03 0.000 1.00000
## mean_sbp.0 3.965e-02 9.530e-03 4.161 3.19e-05 ***
## high_bp.0 -6.781e-02 1.127e-02 -6.015 1.85e-09 ***
## htn_meds_count.0 1.925e-02 1.224e-02 1.574 0.11562
## f.21003.0.0 2.378e-01 9.346e-03 25.443 < 2e-16 ***
## f.31.0.0 -9.829e-02 8.781e-03 -11.193 < 2e-16 ***
## angina.0 -4.728e-02 9.807e-03 -4.821 1.44e-06 ***
## heartattack.0 -2.870e-02 9.465e-03 -3.033 0.00243 **
## f.2443.0.0 -3.849e-02 9.066e-03 -4.246 2.19e-05 ***
## depr_l.0 -1.403e-01 8.736e-03 -16.058 < 2e-16 ***
## f.21001.0.0 -9.506e-02 9.289e-03 -10.233 < 2e-16 ***
## mean_hr.0 -5.850e-02 8.840e-03 -6.618 3.81e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9383 on 12034 degrees of freedom
## Multiple R-squared: 0.1203, Adjusted R-squared: 0.1195
## F-statistic: 149.7 on 11 and 12034 DF, p-value: < 2.2e-16ANY medication effects
partcipants without/with meds only
### partcipants with/without meds only
# prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, meds_count.0, f.21003.0.0,
f.31.0.0, angina.0, heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0,
mean_hr.0) # without htn_meds_count.0
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
predictors <- c("mean_sbp.0", "high_bp.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0") # without meds_count.0
# depressive mood
outcome <- "depr_c.0"
dat_filtered <- filter(dat, meds_count.0 < 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, depr_c.0, mean_sbp.0, high_bp.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0,
f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0))))
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_meds0 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_meds0)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0781 -0.6207 -0.3118 0.3275 6.1932
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.796e-15 4.257e-03 0.000 1.000
## mean_sbp.0 -7.968e-02 4.986e-03 -15.980 < 2e-16 ***
## high_bp.0 6.803e-02 4.610e-03 14.759 < 2e-16 ***
## f.21003.0.0 -1.467e-01 4.515e-03 -32.497 < 2e-16 ***
## f.31.0.0 -2.726e-02 4.379e-03 -6.225 4.84e-10 ***
## angina.0 4.366e-02 4.276e-03 10.211 < 2e-16 ***
## heartattack.0 8.700e-03 4.278e-03 2.034 0.042 *
## f.2443.0.0 6.613e-04 4.263e-03 0.155 0.877
## depr_l.0 1.465e-01 4.265e-03 34.357 < 2e-16 ***
## f.21001.0.0 3.006e-02 4.451e-03 6.753 1.46e-11 ***
## mean_hr.0 3.475e-02 4.382e-03 7.931 2.22e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.968 on 51687 degrees of freedom
## Multiple R-squared: 0.06317, Adjusted R-squared: 0.06299
## F-statistic: 348.5 on 10 and 51687 DF, p-value: < 2.2e-16dat_filtered <- filter(dat, meds_count.0 > 0)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, depr_c.0, mean_sbp.0, high_bp.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0,
f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0))))
mdl_fit_meds1 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_meds1)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4920 -0.5945 -0.2640 0.3736 5.0063
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.494e-15 1.850e-03 0.00 1
## mean_sbp.0 -5.700e-02 2.064e-03 -27.61 <2e-16 ***
## high_bp.0 2.705e-02 2.026e-03 13.35 <2e-16 ***
## f.21003.0.0 -1.670e-01 1.989e-03 -83.97 <2e-16 ***
## f.31.0.0 -4.335e-02 1.910e-03 -22.70 <2e-16 ***
## angina.0 6.235e-02 1.990e-03 31.33 <2e-16 ***
## heartattack.0 2.016e-02 1.986e-03 10.15 <2e-16 ***
## f.2443.0.0 2.387e-02 1.916e-03 12.46 <2e-16 ***
## depr_l.0 2.714e-01 1.867e-03 145.40 <2e-16 ***
## f.21001.0.0 7.293e-02 1.962e-03 37.18 <2e-16 ***
## mean_hr.0 5.499e-02 1.903e-03 28.90 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9287 on 252062 degrees of freedom
## Multiple R-squared: 0.1375, Adjusted R-squared: 0.1375
## F-statistic: 4020 on 10 and 252062 DF, p-value: < 2.2e-16# well-being
outcome <- "wb.0"
dat_filtered <- filter(dat, meds_count.0 < 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, wb.0, mean_sbp.0, high_bp.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0,
f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0))))
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_wb_meds0 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_wb_meds0)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4931 -0.6261 0.0200 0.6454 3.2344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.259e-15 6.262e-03 0.000 1.0000
## mean_sbp.0 6.674e-02 7.282e-03 9.165 < 2e-16 ***
## high_bp.0 -7.598e-02 6.735e-03 -11.281 < 2e-16 ***
## f.21003.0.0 1.542e-01 6.663e-03 23.138 < 2e-16 ***
## f.31.0.0 -5.586e-02 6.438e-03 -8.677 < 2e-16 ***
## angina.0 -3.159e-02 6.270e-03 -5.038 4.73e-07 ***
## heartattack.0 -8.201e-03 6.279e-03 -1.306 0.1915
## f.2443.0.0 -1.064e-02 6.273e-03 -1.697 0.0898 .
## depr_l.0 -8.375e-02 6.270e-03 -13.357 < 2e-16 ***
## f.21001.0.0 -6.371e-02 6.559e-03 -9.714 < 2e-16 ***
## mean_hr.0 -5.273e-02 6.443e-03 -8.185 2.85e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9716 on 24065 degrees of freedom
## Multiple R-squared: 0.05639, Adjusted R-squared: 0.056
## F-statistic: 143.8 on 10 and 24065 DF, p-value: < 2.2e-16dat_filtered <- filter(dat, meds_count.0 > 0)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, wb.0, mean_sbp.0, high_bp.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0,
f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0))))
mdl_fit_wb_meds1 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_wb_meds1)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2472 -0.5887 0.0246 0.6275 4.0415
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.452e-15 2.926e-03 0.000 1
## mean_sbp.0 5.267e-02 3.263e-03 16.145 < 2e-16 ***
## high_bp.0 -4.147e-02 3.204e-03 -12.943 < 2e-16 ***
## f.21003.0.0 1.902e-01 3.149e-03 60.407 < 2e-16 ***
## f.31.0.0 -2.769e-02 3.013e-03 -9.192 < 2e-16 ***
## angina.0 -4.976e-02 3.123e-03 -15.935 < 2e-16 ***
## heartattack.0 -2.287e-02 3.117e-03 -7.337 2.19e-13 ***
## f.2443.0.0 -3.685e-02 3.028e-03 -12.169 < 2e-16 ***
## depr_l.0 -1.723e-01 2.952e-03 -58.365 < 2e-16 ***
## f.21001.0.0 -7.922e-02 3.100e-03 -25.556 < 2e-16 ***
## mean_hr.0 -4.714e-02 3.012e-03 -15.649 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9518 on 105789 degrees of freedom
## Multiple R-squared: 0.09407, Adjusted R-squared: 0.09398
## F-statistic: 1098 on 10 and 105789 DF, p-value: < 2.2e-16BP ELEVATING medication effects
# BP ELEVATING meds
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
elev_meds_count.0, f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0", "elev_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, elev_meds_count.0, f.21003.0.0,
f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5356 -0.5959 -0.2749 0.3707 5.1654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.828e-15 1.697e-03 0.00 1
## f.21003.0.0 -1.690e-01 1.722e-03 -98.13 <2e-16 ***
## f.31.0.0 -5.334e-02 1.728e-03 -30.86 <2e-16 ***
## angina.0 6.602e-02 1.821e-03 36.26 <2e-16 ***
## heartattack.0 2.463e-02 1.816e-03 13.56 <2e-16 ***
## f.2443.0.0 3.176e-02 1.747e-03 18.18 <2e-16 ***
## depr_l.0 2.662e-01 1.707e-03 155.95 <2e-16 ***
## f.21001.0.0 7.036e-02 1.753e-03 40.14 <2e-16 ***
## mean_hr.0 5.221e-02 1.740e-03 30.01 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9352 on 303762 degrees of freedom
## Multiple R-squared: 0.1254, Adjusted R-squared: 0.1254
## F-statistic: 5446 on 8 and 303762 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit111 <- lm(mdl, data=dat_scaled)
summary(mdl_fit111)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4915 -0.5928 -0.2702 0.3691 5.2064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.583e-15 1.689e-03 0.000 1
## mean_sbp.0 -6.192e-02 1.908e-03 -32.456 < 2e-16 ***
## high_bp.0 4.277e-02 2.222e-03 19.251 < 2e-16 ***
## htn_meds_count.0 -1.307e-02 2.371e-03 -5.510 3.59e-08 ***
## elev_meds_count.0 7.182e-02 1.769e-03 40.601 < 2e-16 ***
## f.21003.0.0 -1.580e-01 1.840e-03 -85.880 < 2e-16 ***
## f.31.0.0 -4.610e-02 1.739e-03 -26.506 < 2e-16 ***
## angina.0 5.376e-02 1.887e-03 28.488 < 2e-16 ***
## heartattack.0 1.471e-02 1.862e-03 7.901 2.78e-15 ***
## f.2443.0.0 2.119e-02 1.772e-03 11.959 < 2e-16 ***
## depr_l.0 2.627e-01 1.702e-03 154.356 < 2e-16 ***
## f.21001.0.0 6.725e-02 1.810e-03 37.154 < 2e-16 ***
## mean_hr.0 5.393e-02 1.740e-03 30.994 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9307 on 303758 degrees of freedom
## Multiple R-squared: 0.1338, Adjusted R-squared: 0.1338
## F-statistic: 3911 on 12 and 303758 DF, p-value: < 2.2e-16car::vif(mdl_fit111)## mean_sbp.0 high_bp.0 htn_meds_count.0 elev_meds_count.0
## 1.276535 1.730798 1.972247 1.097364
## f.21003.0.0 f.31.0.0 angina.0 heartattack.0
## 1.186855 1.060785 1.248819 1.215530
## f.2443.0.0 depr_l.0 f.21001.0.0 mean_hr.0
## 1.100670 1.015405 1.148878 1.061911# calculate delta adj. r squared
summary(mdl_fit111)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.008373589### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, elevmeds_ctgry.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- "elevmeds_ctgry.0"
covs <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0","f.21003.0.0", "f.31.0.0", "angina.0",
"heartattack.0", "f.2443.0.0", "depr_l.0", "f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
mdl <- as.formula(paste(paste(outcome), paste(predictors, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -1.15909, -0.43402, -0.18402, 0.23826, 2.62011
##
## Coefficients:
## Estimate
## (Intercept) 1.566038
## elevmeds_ctgry.0acemetacin -0.154273
## elevmeds_ctgry.0adrenaline product -0.071720
## elevmeds_ctgry.0amphotericin 0.843053
## elevmeds_ctgry.0anadin tablet 0.035400
## elevmeds_ctgry.0arcoxia 120mg tablet 0.049634
## elevmeds_ctgry.0arcoxia 60mg tablet -0.025157
## elevmeds_ctgry.0arcoxia 90mg tablet -0.089402
## elevmeds_ctgry.0arthrotec 50 tablet -0.046912
## elevmeds_ctgry.0arthrotec tablet -0.018400
## elevmeds_ctgry.0aspav dispersible tablet 0.267296
## elevmeds_ctgry.0aspirin -0.132016
## elevmeds_ctgry.0aspirin 75mg tablet -0.186151
## elevmeds_ctgry.0brufen 200mg tablet -0.101752
## elevmeds_ctgry.0carbamazepine 0.004645
## elevmeds_ctgry.0carbamazepine product 0.055174
## elevmeds_ctgry.0celebrex 100mg capsule 0.023962
## elevmeds_ctgry.0celebrex 200mg capsule -0.023044
## elevmeds_ctgry.0co-phenotrope 0.159769
## elevmeds_ctgry.0cortisone -0.135187
## elevmeds_ctgry.0cuprofen 200mg tablet -0.127013
## elevmeds_ctgry.0cya - cyclosporin -0.149371
## elevmeds_ctgry.0desmopressin product -0.173181
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.093053
## elevmeds_ctgry.0dexamethasone 0.044427
## elevmeds_ctgry.0diclofenac sodium+misoprostol -0.094547
## elevmeds_ctgry.0dicloflex 25mg e/c tablet -0.063854
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -0.085569
## elevmeds_ctgry.0dihydrocodeine 0.272462
## elevmeds_ctgry.0dipyridamole+aspirin -0.131827
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.029200
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.092786
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.008345
## elevmeds_ctgry.0ephedrine 0.408962
## elevmeds_ctgry.0epinephrine -0.099822
## elevmeds_ctgry.0epoetin beta 0.255391
## elevmeds_ctgry.0etodolac 0.027450
## elevmeds_ctgry.0etoricoxib 0.009824
## elevmeds_ctgry.0felbinac 0.130391
## elevmeds_ctgry.0fenactol 25mg e/c tablet -0.281947
## elevmeds_ctgry.0florinef 100mcg tablet -0.343816
## elevmeds_ctgry.0fludrocortisone -0.008895
## elevmeds_ctgry.0flurbiprofen -0.114115
## elevmeds_ctgry.0froben 50mg tablet 0.108962
## elevmeds_ctgry.0gabapentin 0.274380
## elevmeds_ctgry.0goserelin -0.186727
## elevmeds_ctgry.0growth hormone product -0.009220
## elevmeds_ctgry.0hydrocortisone -0.015038
## elevmeds_ctgry.0hydrocortisone product -0.016038
## elevmeds_ctgry.0hydrocortisone+clotrimazole -0.132704
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.543337
## elevmeds_ctgry.0hydrocortisone+miconazole 0.031184
## elevmeds_ctgry.0ibuprofen -0.054302
## elevmeds_ctgry.0ibuprofen product -0.045204
## elevmeds_ctgry.0indomethacin -0.088510
## elevmeds_ctgry.0indomethacin product -0.216038
## elevmeds_ctgry.0ketoprofen -0.033143
## elevmeds_ctgry.0ketotifen -0.038260
## elevmeds_ctgry.0lamictal 25mg tablet 0.046207
## elevmeds_ctgry.0liothyronine 0.050845
## elevmeds_ctgry.0lithium product 0.290067
## elevmeds_ctgry.0lyrica 25mg capsule 0.223962
## elevmeds_ctgry.0meloxicam -0.049332
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.028538
## elevmeds_ctgry.0methylprednisolone 0.320326
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.112913
## elevmeds_ctgry.0migril tablet -0.136933
## elevmeds_ctgry.0mobic 15mg tablet -0.033780
## elevmeds_ctgry.0mobic 7.5mg tablet 0.105015
## elevmeds_ctgry.0modafinil 0.314644
## elevmeds_ctgry.0nabumetone -0.015635
## elevmeds_ctgry.0napratec tablet combination pack -0.101752
## elevmeds_ctgry.0naprosyn 250mg tablet -0.071668
## elevmeds_ctgry.0naproxen -0.087217
## elevmeds_ctgry.0neurontin 100mg capsule 0.058962
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.183962
## elevmeds_ctgry.0nuelin 125mg tablet 0.233962
## elevmeds_ctgry.0nurofen 200mg tablet -0.001332
## elevmeds_ctgry.0orphenadrine -0.177149
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.068578
## elevmeds_ctgry.0oxcarbazepine 0.183962
## elevmeds_ctgry.0piroxicam -0.115587
## elevmeds_ctgry.0ponstan 250mg capsule -0.034788
## elevmeds_ctgry.0prednesol 5mg tablet -0.232704
## elevmeds_ctgry.0prednisolone -0.049967
## elevmeds_ctgry.0prednisolone product -0.029452
## elevmeds_ctgry.0prednisone -0.046375
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.138508
## elevmeds_ctgry.0provigil 100mg tablet 0.093053
## elevmeds_ctgry.0pseudoephedrine -0.081663
## elevmeds_ctgry.0salicylic acid product -0.191038
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule -0.258345
## elevmeds_ctgry.0sodium thyroxine -0.125159
## elevmeds_ctgry.0sodium valproate 0.095011
## elevmeds_ctgry.0somatropin 0.246462
## elevmeds_ctgry.0surgam 200mg tablet 0.121462
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.005391
## elevmeds_ctgry.0tenoxicam 0.133962
## elevmeds_ctgry.0theophylline product 0.093962
## elevmeds_ctgry.0thyroxine product -0.128605
## elevmeds_ctgry.0thyroxine sodium -0.118867
## elevmeds_ctgry.0triamcinolone -0.089026
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet -0.159788
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet -0.008345
## elevmeds_ctgry.0voltarene 25mg e/c tablet -0.241038
## Std. Error
## (Intercept) 0.079088
## elevmeds_ctgry.0acemetacin 0.160486
## elevmeds_ctgry.0adrenaline product 0.117428
## elevmeds_ctgry.0amphotericin 0.190768
## elevmeds_ctgry.0anadin tablet 0.085522
## elevmeds_ctgry.0arcoxia 120mg tablet 0.105844
## elevmeds_ctgry.0arcoxia 60mg tablet 0.091323
## elevmeds_ctgry.0arcoxia 90mg tablet 0.096712
## elevmeds_ctgry.0arthrotec 50 tablet 0.084621
## elevmeds_ctgry.0arthrotec tablet 0.082323
## elevmeds_ctgry.0aspav dispersible tablet 0.184068
## elevmeds_ctgry.0aspirin 0.079124
## elevmeds_ctgry.0aspirin 75mg tablet 0.079964
## elevmeds_ctgry.0brufen 200mg tablet 0.110340
## elevmeds_ctgry.0carbamazepine 0.082970
## elevmeds_ctgry.0carbamazepine product 0.127674
## elevmeds_ctgry.0celebrex 100mg capsule 0.085294
## elevmeds_ctgry.0celebrex 200mg capsule 0.091469
## elevmeds_ctgry.0co-phenotrope 0.130188
## elevmeds_ctgry.0cortisone 0.115362
## elevmeds_ctgry.0cuprofen 200mg tablet 0.119752
## elevmeds_ctgry.0cya - cyclosporin 0.168392
## elevmeds_ctgry.0desmopressin product 0.231547
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.190768
## elevmeds_ctgry.0dexamethasone 0.100547
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.087801
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.083539
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.106934
## elevmeds_ctgry.0dihydrocodeine 0.080949
## elevmeds_ctgry.0dipyridamole+aspirin 0.153958
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.148463
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.100772
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.178202
## elevmeds_ctgry.0ephedrine 0.198510
## elevmeds_ctgry.0epinephrine 0.123348
## elevmeds_ctgry.0epoetin beta 0.231547
## elevmeds_ctgry.0etodolac 0.087452
## elevmeds_ctgry.0etoricoxib 0.086012
## elevmeds_ctgry.0felbinac 0.173016
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.146026
## elevmeds_ctgry.0florinef 100mcg tablet 0.207580
## elevmeds_ctgry.0fludrocortisone 0.092860
## elevmeds_ctgry.0flurbiprofen 0.137860
## elevmeds_ctgry.0froben 50mg tablet 0.198510
## elevmeds_ctgry.0gabapentin 0.080584
## elevmeds_ctgry.0goserelin 0.132990
## elevmeds_ctgry.0growth hormone product 0.146026
## elevmeds_ctgry.0hydrocortisone 0.083174
## elevmeds_ctgry.0hydrocortisone product 0.087554
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.168392
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.164239
## elevmeds_ctgry.0hydrocortisone+miconazole 0.157074
## elevmeds_ctgry.0ibuprofen 0.079132
## elevmeds_ctgry.0ibuprofen product 0.114723
## elevmeds_ctgry.0indomethacin 0.090096
## elevmeds_ctgry.0indomethacin product 0.269365
## elevmeds_ctgry.0ketoprofen 0.103039
## elevmeds_ctgry.0ketotifen 0.207580
## elevmeds_ctgry.0lamictal 25mg tablet 0.114107
## elevmeds_ctgry.0liothyronine 0.102763
## elevmeds_ctgry.0lithium product 0.084963
## elevmeds_ctgry.0lyrica 25mg capsule 0.103320
## elevmeds_ctgry.0meloxicam 0.081508
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.151098
## elevmeds_ctgry.0methylprednisolone 0.190768
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.164239
## elevmeds_ctgry.0migril tablet 0.105844
## elevmeds_ctgry.0mobic 15mg tablet 0.130188
## elevmeds_ctgry.0mobic 7.5mg tablet 0.153958
## elevmeds_ctgry.0modafinil 0.117428
## elevmeds_ctgry.0nabumetone 0.094490
## elevmeds_ctgry.0napratec tablet combination pack 0.173016
## elevmeds_ctgry.0naprosyn 250mg tablet 0.088024
## elevmeds_ctgry.0naproxen 0.080240
## elevmeds_ctgry.0neurontin 100mg capsule 0.137860
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.341700
## elevmeds_ctgry.0nuelin 125mg tablet 0.168392
## elevmeds_ctgry.0nurofen 200mg tablet 0.100772
## elevmeds_ctgry.0orphenadrine 0.157074
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.178202
## elevmeds_ctgry.0oxcarbazepine 0.143766
## elevmeds_ctgry.0piroxicam 0.096133
## elevmeds_ctgry.0ponstan 250mg capsule 0.120593
## elevmeds_ctgry.0prednesol 5mg tablet 0.168392
## elevmeds_ctgry.0prednisolone 0.080349
## elevmeds_ctgry.0prednisolone product 0.101478
## elevmeds_ctgry.0prednisone 0.090096
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.088550
## elevmeds_ctgry.0provigil 100mg tablet 0.190768
## elevmeds_ctgry.0pseudoephedrine 0.128898
## elevmeds_ctgry.0salicylic acid product 0.198510
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.178202
## elevmeds_ctgry.0sodium thyroxine 0.092168
## elevmeds_ctgry.0sodium valproate 0.086583
## elevmeds_ctgry.0somatropin 0.298552
## elevmeds_ctgry.0surgam 200mg tablet 0.218390
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.231547
## elevmeds_ctgry.0tenoxicam 0.198510
## elevmeds_ctgry.0theophylline product 0.139698
## elevmeds_ctgry.0thyroxine product 0.079653
## elevmeds_ctgry.0thyroxine sodium 0.081472
## elevmeds_ctgry.0triamcinolone 0.100327
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.110340
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.121471
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.198510
## t value
## (Intercept) 19.801
## elevmeds_ctgry.0acemetacin -0.961
## elevmeds_ctgry.0adrenaline product -0.611
## elevmeds_ctgry.0amphotericin 4.419
## elevmeds_ctgry.0anadin tablet 0.414
## elevmeds_ctgry.0arcoxia 120mg tablet 0.469
## elevmeds_ctgry.0arcoxia 60mg tablet -0.275
## elevmeds_ctgry.0arcoxia 90mg tablet -0.924
## elevmeds_ctgry.0arthrotec 50 tablet -0.554
## elevmeds_ctgry.0arthrotec tablet -0.224
## elevmeds_ctgry.0aspav dispersible tablet 1.452
## elevmeds_ctgry.0aspirin -1.668
## elevmeds_ctgry.0aspirin 75mg tablet -2.328
## elevmeds_ctgry.0brufen 200mg tablet -0.922
## elevmeds_ctgry.0carbamazepine 0.056
## elevmeds_ctgry.0carbamazepine product 0.432
## elevmeds_ctgry.0celebrex 100mg capsule 0.281
## elevmeds_ctgry.0celebrex 200mg capsule -0.252
## elevmeds_ctgry.0co-phenotrope 1.227
## elevmeds_ctgry.0cortisone -1.172
## elevmeds_ctgry.0cuprofen 200mg tablet -1.061
## elevmeds_ctgry.0cya - cyclosporin -0.887
## elevmeds_ctgry.0desmopressin product -0.748
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.488
## elevmeds_ctgry.0dexamethasone 0.442
## elevmeds_ctgry.0diclofenac sodium+misoprostol -1.077
## elevmeds_ctgry.0dicloflex 25mg e/c tablet -0.764
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -0.800
## elevmeds_ctgry.0dihydrocodeine 3.366
## elevmeds_ctgry.0dipyridamole+aspirin -0.856
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.197
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.921
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.047
## elevmeds_ctgry.0ephedrine 2.060
## elevmeds_ctgry.0epinephrine -0.809
## elevmeds_ctgry.0epoetin beta 1.103
## elevmeds_ctgry.0etodolac 0.314
## elevmeds_ctgry.0etoricoxib 0.114
## elevmeds_ctgry.0felbinac 0.754
## elevmeds_ctgry.0fenactol 25mg e/c tablet -1.931
## elevmeds_ctgry.0florinef 100mcg tablet -1.656
## elevmeds_ctgry.0fludrocortisone -0.096
## elevmeds_ctgry.0flurbiprofen -0.828
## elevmeds_ctgry.0froben 50mg tablet 0.549
## elevmeds_ctgry.0gabapentin 3.405
## elevmeds_ctgry.0goserelin -1.404
## elevmeds_ctgry.0growth hormone product -0.063
## elevmeds_ctgry.0hydrocortisone -0.181
## elevmeds_ctgry.0hydrocortisone product -0.183
## elevmeds_ctgry.0hydrocortisone+clotrimazole -0.788
## elevmeds_ctgry.0hydrocortisone+lidocaine 3.308
## elevmeds_ctgry.0hydrocortisone+miconazole 0.199
## elevmeds_ctgry.0ibuprofen -0.686
## elevmeds_ctgry.0ibuprofen product -0.394
## elevmeds_ctgry.0indomethacin -0.982
## elevmeds_ctgry.0indomethacin product -0.802
## elevmeds_ctgry.0ketoprofen -0.322
## elevmeds_ctgry.0ketotifen -0.184
## elevmeds_ctgry.0lamictal 25mg tablet 0.405
## elevmeds_ctgry.0liothyronine 0.495
## elevmeds_ctgry.0lithium product 3.414
## elevmeds_ctgry.0lyrica 25mg capsule 2.168
## elevmeds_ctgry.0meloxicam -0.605
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.189
## elevmeds_ctgry.0methylprednisolone 1.679
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.687
## elevmeds_ctgry.0migril tablet -1.294
## elevmeds_ctgry.0mobic 15mg tablet -0.259
## elevmeds_ctgry.0mobic 7.5mg tablet 0.682
## elevmeds_ctgry.0modafinil 2.679
## elevmeds_ctgry.0nabumetone -0.165
## elevmeds_ctgry.0napratec tablet combination pack -0.588
## elevmeds_ctgry.0naprosyn 250mg tablet -0.814
## elevmeds_ctgry.0naproxen -1.087
## elevmeds_ctgry.0neurontin 100mg capsule 0.428
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.538
## elevmeds_ctgry.0nuelin 125mg tablet 1.389
## elevmeds_ctgry.0nurofen 200mg tablet -0.013
## elevmeds_ctgry.0orphenadrine -1.128
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.385
## elevmeds_ctgry.0oxcarbazepine 1.280
## elevmeds_ctgry.0piroxicam -1.202
## elevmeds_ctgry.0ponstan 250mg capsule -0.288
## elevmeds_ctgry.0prednesol 5mg tablet -1.382
## elevmeds_ctgry.0prednisolone -0.622
## elevmeds_ctgry.0prednisolone product -0.290
## elevmeds_ctgry.0prednisone -0.515
## elevmeds_ctgry.0priadel 200mg m/r tablet 1.564
## elevmeds_ctgry.0provigil 100mg tablet 0.488
## elevmeds_ctgry.0pseudoephedrine -0.634
## elevmeds_ctgry.0salicylic acid product -0.962
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule -1.450
## elevmeds_ctgry.0sodium thyroxine -1.358
## elevmeds_ctgry.0sodium valproate 1.097
## elevmeds_ctgry.0somatropin 0.826
## elevmeds_ctgry.0surgam 200mg tablet 0.556
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.023
## elevmeds_ctgry.0tenoxicam 0.675
## elevmeds_ctgry.0theophylline product 0.673
## elevmeds_ctgry.0thyroxine product -1.615
## elevmeds_ctgry.0thyroxine sodium -1.459
## elevmeds_ctgry.0triamcinolone -0.887
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet -1.448
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet -0.069
## elevmeds_ctgry.0voltarene 25mg e/c tablet -1.214
## Pr(>|t|)
## (Intercept) < 2e-16
## elevmeds_ctgry.0acemetacin 0.336409
## elevmeds_ctgry.0adrenaline product 0.541364
## elevmeds_ctgry.0amphotericin 9.91e-06
## elevmeds_ctgry.0anadin tablet 0.678929
## elevmeds_ctgry.0arcoxia 120mg tablet 0.639116
## elevmeds_ctgry.0arcoxia 60mg tablet 0.782952
## elevmeds_ctgry.0arcoxia 90mg tablet 0.355270
## elevmeds_ctgry.0arthrotec 50 tablet 0.579321
## elevmeds_ctgry.0arthrotec tablet 0.823139
## elevmeds_ctgry.0aspav dispersible tablet 0.146460
## elevmeds_ctgry.0aspirin 0.095226
## elevmeds_ctgry.0aspirin 75mg tablet 0.019917
## elevmeds_ctgry.0brufen 200mg tablet 0.356441
## elevmeds_ctgry.0carbamazepine 0.955351
## elevmeds_ctgry.0carbamazepine product 0.665634
## elevmeds_ctgry.0celebrex 100mg capsule 0.778758
## elevmeds_ctgry.0celebrex 200mg capsule 0.801092
## elevmeds_ctgry.0co-phenotrope 0.219744
## elevmeds_ctgry.0cortisone 0.241261
## elevmeds_ctgry.0cuprofen 200mg tablet 0.288858
## elevmeds_ctgry.0cya - cyclosporin 0.375056
## elevmeds_ctgry.0desmopressin product 0.454504
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.625705
## elevmeds_ctgry.0dexamethasone 0.658594
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.281557
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.444651
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.423593
## elevmeds_ctgry.0dihydrocodeine 0.000763
## elevmeds_ctgry.0dipyridamole+aspirin 0.391858
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.844075
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.357185
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.962648
## elevmeds_ctgry.0ephedrine 0.039385
## elevmeds_ctgry.0epinephrine 0.418363
## elevmeds_ctgry.0epoetin beta 0.270039
## elevmeds_ctgry.0etodolac 0.753611
## elevmeds_ctgry.0etoricoxib 0.909063
## elevmeds_ctgry.0felbinac 0.451069
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.053511
## elevmeds_ctgry.0florinef 100mcg tablet 0.097663
## elevmeds_ctgry.0fludrocortisone 0.923689
## elevmeds_ctgry.0flurbiprofen 0.407810
## elevmeds_ctgry.0froben 50mg tablet 0.583074
## elevmeds_ctgry.0gabapentin 0.000662
## elevmeds_ctgry.0goserelin 0.160301
## elevmeds_ctgry.0growth hormone product 0.949658
## elevmeds_ctgry.0hydrocortisone 0.856527
## elevmeds_ctgry.0hydrocortisone product 0.854660
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.430657
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.000939
## elevmeds_ctgry.0hydrocortisone+miconazole 0.842628
## elevmeds_ctgry.0ibuprofen 0.492576
## elevmeds_ctgry.0ibuprofen product 0.693560
## elevmeds_ctgry.0indomethacin 0.325911
## elevmeds_ctgry.0indomethacin product 0.422539
## elevmeds_ctgry.0ketoprofen 0.747714
## elevmeds_ctgry.0ketotifen 0.853768
## elevmeds_ctgry.0lamictal 25mg tablet 0.685519
## elevmeds_ctgry.0liothyronine 0.620755
## elevmeds_ctgry.0lithium product 0.000640
## elevmeds_ctgry.0lyrica 25mg capsule 0.030187
## elevmeds_ctgry.0meloxicam 0.545021
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.850196
## elevmeds_ctgry.0methylprednisolone 0.093127
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.491775
## elevmeds_ctgry.0migril tablet 0.195761
## elevmeds_ctgry.0mobic 15mg tablet 0.795274
## elevmeds_ctgry.0mobic 7.5mg tablet 0.495175
## elevmeds_ctgry.0modafinil 0.007375
## elevmeds_ctgry.0nabumetone 0.868581
## elevmeds_ctgry.0napratec tablet combination pack 0.556460
## elevmeds_ctgry.0naprosyn 250mg tablet 0.415538
## elevmeds_ctgry.0naproxen 0.277061
## elevmeds_ctgry.0neurontin 100mg capsule 0.668873
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.590320
## elevmeds_ctgry.0nuelin 125mg tablet 0.164716
## elevmeds_ctgry.0nurofen 200mg tablet 0.989455
## elevmeds_ctgry.0orphenadrine 0.259405
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.700363
## elevmeds_ctgry.0oxcarbazepine 0.200688
## elevmeds_ctgry.0piroxicam 0.229223
## elevmeds_ctgry.0ponstan 250mg capsule 0.772986
## elevmeds_ctgry.0prednesol 5mg tablet 0.166998
## elevmeds_ctgry.0prednisolone 0.534023
## elevmeds_ctgry.0prednisolone product 0.771638
## elevmeds_ctgry.0prednisone 0.606747
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.117780
## elevmeds_ctgry.0provigil 100mg tablet 0.625705
## elevmeds_ctgry.0pseudoephedrine 0.526379
## elevmeds_ctgry.0salicylic acid product 0.335871
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.147135
## elevmeds_ctgry.0sodium thyroxine 0.174481
## elevmeds_ctgry.0sodium valproate 0.272493
## elevmeds_ctgry.0somatropin 0.409074
## elevmeds_ctgry.0surgam 200mg tablet 0.578094
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.981425
## elevmeds_ctgry.0tenoxicam 0.499779
## elevmeds_ctgry.0theophylline product 0.501196
## elevmeds_ctgry.0thyroxine product 0.106405
## elevmeds_ctgry.0thyroxine sodium 0.144572
## elevmeds_ctgry.0triamcinolone 0.374885
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.147579
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.945226
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.224660
##
## (Intercept) ***
## elevmeds_ctgry.0acemetacin
## elevmeds_ctgry.0adrenaline product
## elevmeds_ctgry.0amphotericin ***
## elevmeds_ctgry.0anadin tablet
## elevmeds_ctgry.0arcoxia 120mg tablet
## elevmeds_ctgry.0arcoxia 60mg tablet
## elevmeds_ctgry.0arcoxia 90mg tablet
## elevmeds_ctgry.0arthrotec 50 tablet
## elevmeds_ctgry.0arthrotec tablet
## elevmeds_ctgry.0aspav dispersible tablet
## elevmeds_ctgry.0aspirin .
## elevmeds_ctgry.0aspirin 75mg tablet *
## elevmeds_ctgry.0brufen 200mg tablet
## elevmeds_ctgry.0carbamazepine
## elevmeds_ctgry.0carbamazepine product
## elevmeds_ctgry.0celebrex 100mg capsule
## elevmeds_ctgry.0celebrex 200mg capsule
## elevmeds_ctgry.0co-phenotrope
## elevmeds_ctgry.0cortisone
## elevmeds_ctgry.0cuprofen 200mg tablet
## elevmeds_ctgry.0cya - cyclosporin
## elevmeds_ctgry.0desmopressin product
## elevmeds_ctgry.0desmospray 10micrograms nasal spray
## elevmeds_ctgry.0dexamethasone
## elevmeds_ctgry.0diclofenac sodium+misoprostol
## elevmeds_ctgry.0dicloflex 25mg e/c tablet
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule
## elevmeds_ctgry.0dihydrocodeine ***
## elevmeds_ctgry.0dipyridamole+aspirin
## elevmeds_ctgry.0eccoxolac 300mg capsule
## elevmeds_ctgry.0eltroxin 25micrograms tablet
## elevmeds_ctgry.0entocort cr 3mg m/r capsule
## elevmeds_ctgry.0ephedrine *
## elevmeds_ctgry.0epinephrine
## elevmeds_ctgry.0epoetin beta
## elevmeds_ctgry.0etodolac
## elevmeds_ctgry.0etoricoxib
## elevmeds_ctgry.0felbinac
## elevmeds_ctgry.0fenactol 25mg e/c tablet .
## elevmeds_ctgry.0florinef 100mcg tablet .
## elevmeds_ctgry.0fludrocortisone
## elevmeds_ctgry.0flurbiprofen
## elevmeds_ctgry.0froben 50mg tablet
## elevmeds_ctgry.0gabapentin ***
## elevmeds_ctgry.0goserelin
## elevmeds_ctgry.0growth hormone product
## elevmeds_ctgry.0hydrocortisone
## elevmeds_ctgry.0hydrocortisone product
## elevmeds_ctgry.0hydrocortisone+clotrimazole
## elevmeds_ctgry.0hydrocortisone+lidocaine ***
## elevmeds_ctgry.0hydrocortisone+miconazole
## elevmeds_ctgry.0ibuprofen
## elevmeds_ctgry.0ibuprofen product
## elevmeds_ctgry.0indomethacin
## elevmeds_ctgry.0indomethacin product
## elevmeds_ctgry.0ketoprofen
## elevmeds_ctgry.0ketotifen
## elevmeds_ctgry.0lamictal 25mg tablet
## elevmeds_ctgry.0liothyronine
## elevmeds_ctgry.0lithium product ***
## elevmeds_ctgry.0lyrica 25mg capsule *
## elevmeds_ctgry.0meloxicam
## elevmeds_ctgry.0mesren mr 400mg m/r tablet
## elevmeds_ctgry.0methylprednisolone .
## elevmeds_ctgry.0micropirin 75mg e/c tablet
## elevmeds_ctgry.0migril tablet
## elevmeds_ctgry.0mobic 15mg tablet
## elevmeds_ctgry.0mobic 7.5mg tablet
## elevmeds_ctgry.0modafinil **
## elevmeds_ctgry.0nabumetone
## elevmeds_ctgry.0napratec tablet combination pack
## elevmeds_ctgry.0naprosyn 250mg tablet
## elevmeds_ctgry.0naproxen
## elevmeds_ctgry.0neurontin 100mg capsule
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent
## elevmeds_ctgry.0nuelin 125mg tablet
## elevmeds_ctgry.0nurofen 200mg tablet
## elevmeds_ctgry.0orphenadrine
## elevmeds_ctgry.0oruvail 100 m/r capsule
## elevmeds_ctgry.0oxcarbazepine
## elevmeds_ctgry.0piroxicam
## elevmeds_ctgry.0ponstan 250mg capsule
## elevmeds_ctgry.0prednesol 5mg tablet
## elevmeds_ctgry.0prednisolone
## elevmeds_ctgry.0prednisolone product
## elevmeds_ctgry.0prednisone
## elevmeds_ctgry.0priadel 200mg m/r tablet
## elevmeds_ctgry.0provigil 100mg tablet
## elevmeds_ctgry.0pseudoephedrine
## elevmeds_ctgry.0salicylic acid product
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule
## elevmeds_ctgry.0sodium thyroxine
## elevmeds_ctgry.0sodium valproate
## elevmeds_ctgry.0somatropin
## elevmeds_ctgry.0surgam 200mg tablet
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment
## elevmeds_ctgry.0tenoxicam
## elevmeds_ctgry.0theophylline product
## elevmeds_ctgry.0thyroxine product
## elevmeds_ctgry.0thyroxine sodium
## elevmeds_ctgry.0triamcinolone
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet
## elevmeds_ctgry.0voltarene 25mg e/c tablet
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5758 on 128789 degrees of freedom
## (373600 observations deleted due to missingness)
## Multiple R-squared: 0.01591, Adjusted R-squared: 0.01511
## F-statistic: 20.02 on 104 and 128789 DF, p-value: < 2.2e-16# with covariates
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit777 <- lm(mdl, data=dat)
summary(mdl_fit777)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -1.46796, -0.35058, -0.14916, 0.23099, 2.82147
##
## Coefficients:
## Estimate
## (Intercept) 2.0186467
## elevmeds_ctgry.0acemetacin -0.3740343
## elevmeds_ctgry.0adrenaline product -0.2405792
## elevmeds_ctgry.0amphotericin 0.4699862
## elevmeds_ctgry.0anadin tablet -0.0501652
## elevmeds_ctgry.0arcoxia 120mg tablet 0.0015746
## elevmeds_ctgry.0arcoxia 60mg tablet -0.0336579
## elevmeds_ctgry.0arcoxia 90mg tablet -0.1451171
## elevmeds_ctgry.0arthrotec 50 tablet -0.0140202
## elevmeds_ctgry.0arthrotec tablet -0.0028761
## elevmeds_ctgry.0aspav dispersible tablet 0.0698745
## elevmeds_ctgry.0aspirin -0.1226061
## elevmeds_ctgry.0aspirin 75mg tablet -0.1618092
## elevmeds_ctgry.0brufen 200mg tablet -0.0244060
## elevmeds_ctgry.0carbamazepine -0.0381098
## elevmeds_ctgry.0carbamazepine product 0.0271335
## elevmeds_ctgry.0celebrex 100mg capsule -0.0142600
## elevmeds_ctgry.0celebrex 200mg capsule -0.0252558
## elevmeds_ctgry.0co-phenotrope 0.2162936
## elevmeds_ctgry.0cortisone -0.1183677
## elevmeds_ctgry.0cuprofen 200mg tablet -0.1769256
## elevmeds_ctgry.0cya - cyclosporin -0.1990344
## elevmeds_ctgry.0desmopressin product -0.1717561
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.0823261
## elevmeds_ctgry.0dexamethasone -0.0058444
## elevmeds_ctgry.0diclofenac sodium+misoprostol -0.0963239
## elevmeds_ctgry.0dicloflex 25mg e/c tablet -0.0914534
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -0.0822038
## elevmeds_ctgry.0dihydrocodeine 0.1780359
## elevmeds_ctgry.0dipyridamole+aspirin -0.1036337
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.0479056
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.0277878
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.0281850
## elevmeds_ctgry.0ephedrine 0.2080405
## elevmeds_ctgry.0epinephrine -0.1363893
## elevmeds_ctgry.0epoetin beta 0.0442866
## elevmeds_ctgry.0etodolac -0.0172084
## elevmeds_ctgry.0etoricoxib -0.0363342
## elevmeds_ctgry.0felbinac -0.0331292
## elevmeds_ctgry.0fenactol 25mg e/c tablet -0.1507724
## elevmeds_ctgry.0florinef 100mcg tablet -0.2675394
## elevmeds_ctgry.0fludrocortisone -0.0814684
## elevmeds_ctgry.0flurbiprofen -0.0269717
## elevmeds_ctgry.0froben 50mg tablet 0.5519520
## elevmeds_ctgry.0gabapentin 0.1721857
## elevmeds_ctgry.0goserelin -0.1058569
## elevmeds_ctgry.0growth hormone product -0.0474062
## elevmeds_ctgry.0hydrocortisone -0.0519521
## elevmeds_ctgry.0hydrocortisone product -0.0556489
## elevmeds_ctgry.0hydrocortisone+clotrimazole -0.2703207
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.4898696
## elevmeds_ctgry.0hydrocortisone+miconazole 0.0385268
## elevmeds_ctgry.0ibuprofen -0.0830631
## elevmeds_ctgry.0ibuprofen product -0.0748970
## elevmeds_ctgry.0indomethacin -0.1067807
## elevmeds_ctgry.0indomethacin product -0.1069713
## elevmeds_ctgry.0ketoprofen -0.0568367
## elevmeds_ctgry.0ketotifen 0.0464460
## elevmeds_ctgry.0lamictal 25mg tablet 0.0488693
## elevmeds_ctgry.0liothyronine -0.0238322
## elevmeds_ctgry.0lithium product 0.1154851
## elevmeds_ctgry.0lyrica 25mg capsule 0.1737348
## elevmeds_ctgry.0meloxicam -0.0484154
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.0176983
## elevmeds_ctgry.0methylprednisolone 0.3308632
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.1016209
## elevmeds_ctgry.0migril tablet -0.1174432
## elevmeds_ctgry.0mobic 15mg tablet 0.0227839
## elevmeds_ctgry.0mobic 7.5mg tablet 0.0807738
## elevmeds_ctgry.0modafinil 0.2507249
## elevmeds_ctgry.0nabumetone -0.0422388
## elevmeds_ctgry.0napratec tablet combination pack -0.1324656
## elevmeds_ctgry.0naprosyn 250mg tablet -0.0653498
## elevmeds_ctgry.0naproxen -0.0906655
## elevmeds_ctgry.0neurontin 100mg capsule 0.0468433
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.1817635
## elevmeds_ctgry.0nuelin 125mg tablet 0.1711851
## elevmeds_ctgry.0nurofen 200mg tablet 0.0331552
## elevmeds_ctgry.0orphenadrine -0.2112536
## elevmeds_ctgry.0oruvail 100 m/r capsule -0.0001430
## elevmeds_ctgry.0oxcarbazepine 0.2194531
## elevmeds_ctgry.0piroxicam -0.1115159
## elevmeds_ctgry.0ponstan 250mg capsule -0.0883959
## elevmeds_ctgry.0prednesol 5mg tablet -0.1468100
## elevmeds_ctgry.0prednisolone -0.0433973
## elevmeds_ctgry.0prednisolone product 0.0148070
## elevmeds_ctgry.0prednisone -0.0143042
## elevmeds_ctgry.0priadel 200mg m/r tablet -0.0610164
## elevmeds_ctgry.0provigil 100mg tablet 0.0921284
## elevmeds_ctgry.0pseudoephedrine -0.1499174
## elevmeds_ctgry.0salicylic acid product -0.1245355
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule -0.3951325
## elevmeds_ctgry.0sodium thyroxine -0.1284680
## elevmeds_ctgry.0sodium valproate 0.0248869
## elevmeds_ctgry.0somatropin 0.1664995
## elevmeds_ctgry.0surgam 200mg tablet 0.1340266
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.0134316
## elevmeds_ctgry.0tenoxicam 0.1031555
## elevmeds_ctgry.0theophylline product 0.0253808
## elevmeds_ctgry.0thyroxine product -0.1408596
## elevmeds_ctgry.0thyroxine sodium -0.1105175
## elevmeds_ctgry.0triamcinolone -0.0875004
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet -0.1695713
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.0159190
## elevmeds_ctgry.0voltarene 25mg e/c tablet -0.3238746
## mean_sbp.0 -0.0016230
## high_bp.0 0.0512476
## htn_meds_count.0 -0.0032229
## f.21003.0.0 -0.0138428
## f.31.0.0 -0.0474235
## angina.0 0.1552243
## heartattack.0 0.0616099
## f.2443.0.0 0.0504031
## depr_l.0 0.5840154
## f.21001.0.0 0.0078112
## mean_hr.0 0.0030052
## Std. Error
## (Intercept) 0.0835646
## elevmeds_ctgry.0acemetacin 0.1754821
## elevmeds_ctgry.0adrenaline product 0.1183669
## elevmeds_ctgry.0amphotericin 0.2198400
## elevmeds_ctgry.0anadin tablet 0.0863507
## elevmeds_ctgry.0arcoxia 120mg tablet 0.1060787
## elevmeds_ctgry.0arcoxia 60mg tablet 0.0913026
## elevmeds_ctgry.0arcoxia 90mg tablet 0.0972799
## elevmeds_ctgry.0arthrotec 50 tablet 0.0849638
## elevmeds_ctgry.0arthrotec tablet 0.0823943
## elevmeds_ctgry.0aspav dispersible tablet 0.1817374
## elevmeds_ctgry.0aspirin 0.0792035
## elevmeds_ctgry.0aspirin 75mg tablet 0.0800649
## elevmeds_ctgry.0brufen 200mg tablet 0.1167153
## elevmeds_ctgry.0carbamazepine 0.0830912
## elevmeds_ctgry.0carbamazepine product 0.1295307
## elevmeds_ctgry.0celebrex 100mg capsule 0.0854163
## elevmeds_ctgry.0celebrex 200mg capsule 0.0924409
## elevmeds_ctgry.0co-phenotrope 0.1295218
## elevmeds_ctgry.0cortisone 0.1138138
## elevmeds_ctgry.0cuprofen 200mg tablet 0.1232213
## elevmeds_ctgry.0cya - cyclosporin 0.1609061
## elevmeds_ctgry.0desmopressin product 0.2198257
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.1817367
## elevmeds_ctgry.0dexamethasone 0.1042281
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.0875496
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.0837390
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.1113343
## elevmeds_ctgry.0dihydrocodeine 0.0810624
## elevmeds_ctgry.0dipyridamole+aspirin 0.1570450
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.1474986
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.0992657
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.1754857
## elevmeds_ctgry.0ephedrine 0.2198178
## elevmeds_ctgry.0epinephrine 0.1281224
## elevmeds_ctgry.0epoetin beta 0.2352372
## elevmeds_ctgry.0etodolac 0.0877440
## elevmeds_ctgry.0etoricoxib 0.0859198
## elevmeds_ctgry.0felbinac 0.1817247
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.1424131
## elevmeds_ctgry.0florinef 100mcg tablet 0.2198127
## elevmeds_ctgry.0fludrocortisone 0.0924540
## elevmeds_ctgry.0flurbiprofen 0.1474989
## elevmeds_ctgry.0froben 50mg tablet 0.2825945
## elevmeds_ctgry.0gabapentin 0.0807222
## elevmeds_ctgry.0goserelin 0.1448666
## elevmeds_ctgry.0growth hormone product 0.1475070
## elevmeds_ctgry.0hydrocortisone 0.0832430
## elevmeds_ctgry.0hydrocortisone product 0.0879251
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.1754877
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.1652055
## elevmeds_ctgry.0hydrocortisone+miconazole 0.1570530
## elevmeds_ctgry.0ibuprofen 0.0792075
## elevmeds_ctgry.0ibuprofen product 0.1201675
## elevmeds_ctgry.0indomethacin 0.0901589
## elevmeds_ctgry.0indomethacin product 0.2826027
## elevmeds_ctgry.0ketoprofen 0.1017443
## elevmeds_ctgry.0ketotifen 0.2075071
## elevmeds_ctgry.0lamictal 25mg tablet 0.1152092
## elevmeds_ctgry.0liothyronine 0.1014754
## elevmeds_ctgry.0lithium product 0.0852090
## elevmeds_ctgry.0lyrica 25mg capsule 0.1053059
## elevmeds_ctgry.0meloxicam 0.0815425
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.1448558
## elevmeds_ctgry.0methylprednisolone 0.1817339
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.1609118
## elevmeds_ctgry.0migril tablet 0.1060842
## elevmeds_ctgry.0mobic 15mg tablet 0.1295207
## elevmeds_ctgry.0mobic 7.5mg tablet 0.1503884
## elevmeds_ctgry.0modafinil 0.1201753
## elevmeds_ctgry.0nabumetone 0.0944180
## elevmeds_ctgry.0napratec tablet combination pack 0.1700178
## elevmeds_ctgry.0naprosyn 250mg tablet 0.0883877
## elevmeds_ctgry.0naproxen 0.0803161
## elevmeds_ctgry.0neurontin 100mg capsule 0.1361209
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.3230847
## elevmeds_ctgry.0nuelin 125mg tablet 0.1652008
## elevmeds_ctgry.0nurofen 200mg tablet 0.1049415
## elevmeds_ctgry.0orphenadrine 0.1535764
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.1754810
## elevmeds_ctgry.0oxcarbazepine 0.1424193
## elevmeds_ctgry.0piroxicam 0.0978287
## elevmeds_ctgry.0ponstan 250mg capsule 0.1232439
## elevmeds_ctgry.0prednesol 5mg tablet 0.1817246
## elevmeds_ctgry.0prednisolone 0.0803770
## elevmeds_ctgry.0prednisolone product 0.1032381
## elevmeds_ctgry.0prednisone 0.0900202
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.0885526
## elevmeds_ctgry.0provigil 100mg tablet 0.1889515
## elevmeds_ctgry.0pseudoephedrine 0.1326067
## elevmeds_ctgry.0salicylic acid product 0.2075106
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.1754854
## elevmeds_ctgry.0sodium thyroxine 0.0920742
## elevmeds_ctgry.0sodium valproate 0.0866726
## elevmeds_ctgry.0somatropin 0.2825904
## elevmeds_ctgry.0surgam 200mg tablet 0.2075045
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.2352223
## elevmeds_ctgry.0tenoxicam 0.1974054
## elevmeds_ctgry.0theophylline product 0.1361242
## elevmeds_ctgry.0thyroxine product 0.0797336
## elevmeds_ctgry.0thyroxine sodium 0.0815805
## elevmeds_ctgry.0triamcinolone 0.1020302
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.1113364
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.1310174
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.2352082
## mean_sbp.0 0.0001020
## high_bp.0 0.0044618
## htn_meds_count.0 0.0021479
## f.21003.0.0 0.0002517
## f.31.0.0 0.0036726
## angina.0 0.0064438
## heartattack.0 0.0071880
## f.2443.0.0 0.0055269
## depr_l.0 0.0064250
## f.21001.0.0 0.0003577
## mean_hr.0 0.0001505
## t value
## (Intercept) 24.157
## elevmeds_ctgry.0acemetacin -2.131
## elevmeds_ctgry.0adrenaline product -2.032
## elevmeds_ctgry.0amphotericin 2.138
## elevmeds_ctgry.0anadin tablet -0.581
## elevmeds_ctgry.0arcoxia 120mg tablet 0.015
## elevmeds_ctgry.0arcoxia 60mg tablet -0.369
## elevmeds_ctgry.0arcoxia 90mg tablet -1.492
## elevmeds_ctgry.0arthrotec 50 tablet -0.165
## elevmeds_ctgry.0arthrotec tablet -0.035
## elevmeds_ctgry.0aspav dispersible tablet 0.384
## elevmeds_ctgry.0aspirin -1.548
## elevmeds_ctgry.0aspirin 75mg tablet -2.021
## elevmeds_ctgry.0brufen 200mg tablet -0.209
## elevmeds_ctgry.0carbamazepine -0.459
## elevmeds_ctgry.0carbamazepine product 0.209
## elevmeds_ctgry.0celebrex 100mg capsule -0.167
## elevmeds_ctgry.0celebrex 200mg capsule -0.273
## elevmeds_ctgry.0co-phenotrope 1.670
## elevmeds_ctgry.0cortisone -1.040
## elevmeds_ctgry.0cuprofen 200mg tablet -1.436
## elevmeds_ctgry.0cya - cyclosporin -1.237
## elevmeds_ctgry.0desmopressin product -0.781
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.453
## elevmeds_ctgry.0dexamethasone -0.056
## elevmeds_ctgry.0diclofenac sodium+misoprostol -1.100
## elevmeds_ctgry.0dicloflex 25mg e/c tablet -1.092
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -0.738
## elevmeds_ctgry.0dihydrocodeine 2.196
## elevmeds_ctgry.0dipyridamole+aspirin -0.660
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.325
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.280
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.161
## elevmeds_ctgry.0ephedrine 0.946
## elevmeds_ctgry.0epinephrine -1.065
## elevmeds_ctgry.0epoetin beta 0.188
## elevmeds_ctgry.0etodolac -0.196
## elevmeds_ctgry.0etoricoxib -0.423
## elevmeds_ctgry.0felbinac -0.182
## elevmeds_ctgry.0fenactol 25mg e/c tablet -1.059
## elevmeds_ctgry.0florinef 100mcg tablet -1.217
## elevmeds_ctgry.0fludrocortisone -0.881
## elevmeds_ctgry.0flurbiprofen -0.183
## elevmeds_ctgry.0froben 50mg tablet 1.953
## elevmeds_ctgry.0gabapentin 2.133
## elevmeds_ctgry.0goserelin -0.731
## elevmeds_ctgry.0growth hormone product -0.321
## elevmeds_ctgry.0hydrocortisone -0.624
## elevmeds_ctgry.0hydrocortisone product -0.633
## elevmeds_ctgry.0hydrocortisone+clotrimazole -1.540
## elevmeds_ctgry.0hydrocortisone+lidocaine 2.965
## elevmeds_ctgry.0hydrocortisone+miconazole 0.245
## elevmeds_ctgry.0ibuprofen -1.049
## elevmeds_ctgry.0ibuprofen product -0.623
## elevmeds_ctgry.0indomethacin -1.184
## elevmeds_ctgry.0indomethacin product -0.379
## elevmeds_ctgry.0ketoprofen -0.559
## elevmeds_ctgry.0ketotifen 0.224
## elevmeds_ctgry.0lamictal 25mg tablet 0.424
## elevmeds_ctgry.0liothyronine -0.235
## elevmeds_ctgry.0lithium product 1.355
## elevmeds_ctgry.0lyrica 25mg capsule 1.650
## elevmeds_ctgry.0meloxicam -0.594
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.122
## elevmeds_ctgry.0methylprednisolone 1.821
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.632
## elevmeds_ctgry.0migril tablet -1.107
## elevmeds_ctgry.0mobic 15mg tablet 0.176
## elevmeds_ctgry.0mobic 7.5mg tablet 0.537
## elevmeds_ctgry.0modafinil 2.086
## elevmeds_ctgry.0nabumetone -0.447
## elevmeds_ctgry.0napratec tablet combination pack -0.779
## elevmeds_ctgry.0naprosyn 250mg tablet -0.739
## elevmeds_ctgry.0naproxen -1.129
## elevmeds_ctgry.0neurontin 100mg capsule 0.344
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.563
## elevmeds_ctgry.0nuelin 125mg tablet 1.036
## elevmeds_ctgry.0nurofen 200mg tablet 0.316
## elevmeds_ctgry.0orphenadrine -1.376
## elevmeds_ctgry.0oruvail 100 m/r capsule -0.001
## elevmeds_ctgry.0oxcarbazepine 1.541
## elevmeds_ctgry.0piroxicam -1.140
## elevmeds_ctgry.0ponstan 250mg capsule -0.717
## elevmeds_ctgry.0prednesol 5mg tablet -0.808
## elevmeds_ctgry.0prednisolone -0.540
## elevmeds_ctgry.0prednisolone product 0.143
## elevmeds_ctgry.0prednisone -0.159
## elevmeds_ctgry.0priadel 200mg m/r tablet -0.689
## elevmeds_ctgry.0provigil 100mg tablet 0.488
## elevmeds_ctgry.0pseudoephedrine -1.131
## elevmeds_ctgry.0salicylic acid product -0.600
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule -2.252
## elevmeds_ctgry.0sodium thyroxine -1.395
## elevmeds_ctgry.0sodium valproate 0.287
## elevmeds_ctgry.0somatropin 0.589
## elevmeds_ctgry.0surgam 200mg tablet 0.646
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.057
## elevmeds_ctgry.0tenoxicam 0.523
## elevmeds_ctgry.0theophylline product 0.186
## elevmeds_ctgry.0thyroxine product -1.767
## elevmeds_ctgry.0thyroxine sodium -1.355
## elevmeds_ctgry.0triamcinolone -0.858
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet -1.523
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.122
## elevmeds_ctgry.0voltarene 25mg e/c tablet -1.377
## mean_sbp.0 -15.907
## high_bp.0 11.486
## htn_meds_count.0 -1.500
## f.21003.0.0 -55.000
## f.31.0.0 -12.913
## angina.0 24.089
## heartattack.0 8.571
## f.2443.0.0 9.120
## depr_l.0 90.898
## f.21001.0.0 21.838
## mean_hr.0 19.967
## Pr(>|t|)
## (Intercept) < 2e-16
## elevmeds_ctgry.0acemetacin 0.03305
## elevmeds_ctgry.0adrenaline product 0.04211
## elevmeds_ctgry.0amphotericin 0.03253
## elevmeds_ctgry.0anadin tablet 0.56128
## elevmeds_ctgry.0arcoxia 120mg tablet 0.98816
## elevmeds_ctgry.0arcoxia 60mg tablet 0.71240
## elevmeds_ctgry.0arcoxia 90mg tablet 0.13577
## elevmeds_ctgry.0arthrotec 50 tablet 0.86893
## elevmeds_ctgry.0arthrotec tablet 0.97215
## elevmeds_ctgry.0aspav dispersible tablet 0.70062
## elevmeds_ctgry.0aspirin 0.12163
## elevmeds_ctgry.0aspirin 75mg tablet 0.04329
## elevmeds_ctgry.0brufen 200mg tablet 0.83437
## elevmeds_ctgry.0carbamazepine 0.64649
## elevmeds_ctgry.0carbamazepine product 0.83408
## elevmeds_ctgry.0celebrex 100mg capsule 0.86741
## elevmeds_ctgry.0celebrex 200mg capsule 0.78469
## elevmeds_ctgry.0co-phenotrope 0.09493
## elevmeds_ctgry.0cortisone 0.29834
## elevmeds_ctgry.0cuprofen 200mg tablet 0.15105
## elevmeds_ctgry.0cya - cyclosporin 0.21611
## elevmeds_ctgry.0desmopressin product 0.43461
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.65055
## elevmeds_ctgry.0dexamethasone 0.95528
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.27124
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.27478
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.46030
## elevmeds_ctgry.0dihydrocodeine 0.02807
## elevmeds_ctgry.0dipyridamole+aspirin 0.50932
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.74534
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.77953
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.87240
## elevmeds_ctgry.0ephedrine 0.34394
## elevmeds_ctgry.0epinephrine 0.28709
## elevmeds_ctgry.0epoetin beta 0.85067
## elevmeds_ctgry.0etodolac 0.84452
## elevmeds_ctgry.0etoricoxib 0.67238
## elevmeds_ctgry.0felbinac 0.85534
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.28974
## elevmeds_ctgry.0florinef 100mcg tablet 0.22356
## elevmeds_ctgry.0fludrocortisone 0.37822
## elevmeds_ctgry.0flurbiprofen 0.85491
## elevmeds_ctgry.0froben 50mg tablet 0.05080
## elevmeds_ctgry.0gabapentin 0.03292
## elevmeds_ctgry.0goserelin 0.46495
## elevmeds_ctgry.0growth hormone product 0.74792
## elevmeds_ctgry.0hydrocortisone 0.53256
## elevmeds_ctgry.0hydrocortisone product 0.52679
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.12347
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.00303
## elevmeds_ctgry.0hydrocortisone+miconazole 0.80622
## elevmeds_ctgry.0ibuprofen 0.29433
## elevmeds_ctgry.0ibuprofen product 0.53311
## elevmeds_ctgry.0indomethacin 0.23627
## elevmeds_ctgry.0indomethacin product 0.70504
## elevmeds_ctgry.0ketoprofen 0.57642
## elevmeds_ctgry.0ketotifen 0.82289
## elevmeds_ctgry.0lamictal 25mg tablet 0.67144
## elevmeds_ctgry.0liothyronine 0.81432
## elevmeds_ctgry.0lithium product 0.17532
## elevmeds_ctgry.0lyrica 25mg capsule 0.09898
## elevmeds_ctgry.0meloxicam 0.55268
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.90276
## elevmeds_ctgry.0methylprednisolone 0.06867
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.52769
## elevmeds_ctgry.0migril tablet 0.26826
## elevmeds_ctgry.0mobic 15mg tablet 0.86037
## elevmeds_ctgry.0mobic 7.5mg tablet 0.59120
## elevmeds_ctgry.0modafinil 0.03695
## elevmeds_ctgry.0nabumetone 0.65462
## elevmeds_ctgry.0napratec tablet combination pack 0.43591
## elevmeds_ctgry.0naprosyn 250mg tablet 0.45969
## elevmeds_ctgry.0naproxen 0.25896
## elevmeds_ctgry.0neurontin 100mg capsule 0.73075
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.57372
## elevmeds_ctgry.0nuelin 125mg tablet 0.30010
## elevmeds_ctgry.0nurofen 200mg tablet 0.75205
## elevmeds_ctgry.0orphenadrine 0.16896
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.99935
## elevmeds_ctgry.0oxcarbazepine 0.12335
## elevmeds_ctgry.0piroxicam 0.25433
## elevmeds_ctgry.0ponstan 250mg capsule 0.47323
## elevmeds_ctgry.0prednesol 5mg tablet 0.41917
## elevmeds_ctgry.0prednisolone 0.58925
## elevmeds_ctgry.0prednisolone product 0.88595
## elevmeds_ctgry.0prednisone 0.87375
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.49080
## elevmeds_ctgry.0provigil 100mg tablet 0.62585
## elevmeds_ctgry.0pseudoephedrine 0.25825
## elevmeds_ctgry.0salicylic acid product 0.54841
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.02435
## elevmeds_ctgry.0sodium thyroxine 0.16294
## elevmeds_ctgry.0sodium valproate 0.77401
## elevmeds_ctgry.0somatropin 0.55574
## elevmeds_ctgry.0surgam 200mg tablet 0.51835
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.95446
## elevmeds_ctgry.0tenoxicam 0.60128
## elevmeds_ctgry.0theophylline product 0.85209
## elevmeds_ctgry.0thyroxine product 0.07729
## elevmeds_ctgry.0thyroxine sodium 0.17551
## elevmeds_ctgry.0triamcinolone 0.39112
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.12775
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.90329
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.16852
## mean_sbp.0 < 2e-16
## high_bp.0 < 2e-16
## htn_meds_count.0 0.13349
## f.21003.0.0 < 2e-16
## f.31.0.0 < 2e-16
## angina.0 < 2e-16
## heartattack.0 < 2e-16
## f.2443.0.0 < 2e-16
## depr_l.0 < 2e-16
## f.21001.0.0 < 2e-16
## mean_hr.0 < 2e-16
##
## (Intercept) ***
## elevmeds_ctgry.0acemetacin *
## elevmeds_ctgry.0adrenaline product *
## elevmeds_ctgry.0amphotericin *
## elevmeds_ctgry.0anadin tablet
## elevmeds_ctgry.0arcoxia 120mg tablet
## elevmeds_ctgry.0arcoxia 60mg tablet
## elevmeds_ctgry.0arcoxia 90mg tablet
## elevmeds_ctgry.0arthrotec 50 tablet
## elevmeds_ctgry.0arthrotec tablet
## elevmeds_ctgry.0aspav dispersible tablet
## elevmeds_ctgry.0aspirin
## elevmeds_ctgry.0aspirin 75mg tablet *
## elevmeds_ctgry.0brufen 200mg tablet
## elevmeds_ctgry.0carbamazepine
## elevmeds_ctgry.0carbamazepine product
## elevmeds_ctgry.0celebrex 100mg capsule
## elevmeds_ctgry.0celebrex 200mg capsule
## elevmeds_ctgry.0co-phenotrope .
## elevmeds_ctgry.0cortisone
## elevmeds_ctgry.0cuprofen 200mg tablet
## elevmeds_ctgry.0cya - cyclosporin
## elevmeds_ctgry.0desmopressin product
## elevmeds_ctgry.0desmospray 10micrograms nasal spray
## elevmeds_ctgry.0dexamethasone
## elevmeds_ctgry.0diclofenac sodium+misoprostol
## elevmeds_ctgry.0dicloflex 25mg e/c tablet
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule
## elevmeds_ctgry.0dihydrocodeine *
## elevmeds_ctgry.0dipyridamole+aspirin
## elevmeds_ctgry.0eccoxolac 300mg capsule
## elevmeds_ctgry.0eltroxin 25micrograms tablet
## elevmeds_ctgry.0entocort cr 3mg m/r capsule
## elevmeds_ctgry.0ephedrine
## elevmeds_ctgry.0epinephrine
## elevmeds_ctgry.0epoetin beta
## elevmeds_ctgry.0etodolac
## elevmeds_ctgry.0etoricoxib
## elevmeds_ctgry.0felbinac
## elevmeds_ctgry.0fenactol 25mg e/c tablet
## elevmeds_ctgry.0florinef 100mcg tablet
## elevmeds_ctgry.0fludrocortisone
## elevmeds_ctgry.0flurbiprofen
## elevmeds_ctgry.0froben 50mg tablet .
## elevmeds_ctgry.0gabapentin *
## elevmeds_ctgry.0goserelin
## elevmeds_ctgry.0growth hormone product
## elevmeds_ctgry.0hydrocortisone
## elevmeds_ctgry.0hydrocortisone product
## elevmeds_ctgry.0hydrocortisone+clotrimazole
## elevmeds_ctgry.0hydrocortisone+lidocaine **
## elevmeds_ctgry.0hydrocortisone+miconazole
## elevmeds_ctgry.0ibuprofen
## elevmeds_ctgry.0ibuprofen product
## elevmeds_ctgry.0indomethacin
## elevmeds_ctgry.0indomethacin product
## elevmeds_ctgry.0ketoprofen
## elevmeds_ctgry.0ketotifen
## elevmeds_ctgry.0lamictal 25mg tablet
## elevmeds_ctgry.0liothyronine
## elevmeds_ctgry.0lithium product
## elevmeds_ctgry.0lyrica 25mg capsule .
## elevmeds_ctgry.0meloxicam
## elevmeds_ctgry.0mesren mr 400mg m/r tablet
## elevmeds_ctgry.0methylprednisolone .
## elevmeds_ctgry.0micropirin 75mg e/c tablet
## elevmeds_ctgry.0migril tablet
## elevmeds_ctgry.0mobic 15mg tablet
## elevmeds_ctgry.0mobic 7.5mg tablet
## elevmeds_ctgry.0modafinil *
## elevmeds_ctgry.0nabumetone
## elevmeds_ctgry.0napratec tablet combination pack
## elevmeds_ctgry.0naprosyn 250mg tablet
## elevmeds_ctgry.0naproxen
## elevmeds_ctgry.0neurontin 100mg capsule
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent
## elevmeds_ctgry.0nuelin 125mg tablet
## elevmeds_ctgry.0nurofen 200mg tablet
## elevmeds_ctgry.0orphenadrine
## elevmeds_ctgry.0oruvail 100 m/r capsule
## elevmeds_ctgry.0oxcarbazepine
## elevmeds_ctgry.0piroxicam
## elevmeds_ctgry.0ponstan 250mg capsule
## elevmeds_ctgry.0prednesol 5mg tablet
## elevmeds_ctgry.0prednisolone
## elevmeds_ctgry.0prednisolone product
## elevmeds_ctgry.0prednisone
## elevmeds_ctgry.0priadel 200mg m/r tablet
## elevmeds_ctgry.0provigil 100mg tablet
## elevmeds_ctgry.0pseudoephedrine
## elevmeds_ctgry.0salicylic acid product
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule *
## elevmeds_ctgry.0sodium thyroxine
## elevmeds_ctgry.0sodium valproate
## elevmeds_ctgry.0somatropin
## elevmeds_ctgry.0surgam 200mg tablet
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment
## elevmeds_ctgry.0tenoxicam
## elevmeds_ctgry.0theophylline product
## elevmeds_ctgry.0thyroxine product .
## elevmeds_ctgry.0thyroxine sodium
## elevmeds_ctgry.0triamcinolone
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet
## elevmeds_ctgry.0voltarene 25mg e/c tablet
## mean_sbp.0 ***
## high_bp.0 ***
## htn_meds_count.0
## f.21003.0.0 ***
## f.31.0.0 ***
## angina.0 ***
## heartattack.0 ***
## f.2443.0.0 ***
## depr_l.0 ***
## f.21001.0.0 ***
## mean_hr.0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5425 on 101302 degrees of freedom
## (401076 observations deleted due to missingness)
## Multiple R-squared: 0.1549, Adjusted R-squared: 0.1539
## F-statistic: 161.4 on 115 and 101302 DF, p-value: < 2.2e-16# calculate delta adj. r squared
summary(mdl_fit777)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.1388179### well-being
outcome <- "wb.0"
mdl <- as.formula(paste(paste(outcome), paste(predictors, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.4278, -0.3661, 0.0026, 0.4158, 1.8479
##
## Coefficients:
## Estimate
## (Intercept) 4.262963
## elevmeds_ctgry.0acemetacin 0.403704
## elevmeds_ctgry.0adrenaline product 0.055556
## elevmeds_ctgry.0amphotericin -0.862963
## elevmeds_ctgry.0anadin tablet 0.158959
## elevmeds_ctgry.0arcoxia 120mg tablet 0.039601
## elevmeds_ctgry.0arcoxia 60mg tablet 0.120596
## elevmeds_ctgry.0arcoxia 90mg tablet -0.024421
## elevmeds_ctgry.0arthrotec 50 tablet 0.153352
## elevmeds_ctgry.0arthrotec tablet 0.078980
## elevmeds_ctgry.0aspav dispersible tablet -0.007407
## elevmeds_ctgry.0aspirin 0.140871
## elevmeds_ctgry.0aspirin 75mg tablet 0.164815
## elevmeds_ctgry.0brufen 200mg tablet -0.186296
## elevmeds_ctgry.0carbamazepine 0.075087
## elevmeds_ctgry.0carbamazepine product -0.444781
## elevmeds_ctgry.0celebrex 100mg capsule 0.162632
## elevmeds_ctgry.0celebrex 200mg capsule 0.191612
## elevmeds_ctgry.0co-phenotrope 0.147454
## elevmeds_ctgry.0cortisone -0.010185
## elevmeds_ctgry.0cuprofen 200mg tablet 0.346128
## elevmeds_ctgry.0cya - cyclosporin -0.462963
## elevmeds_ctgry.0desmopressin product 0.237037
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.157037
## elevmeds_ctgry.0dexamethasone -0.163818
## elevmeds_ctgry.0diclofenac sodium+misoprostol -0.016586
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.038673
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -0.066885
## elevmeds_ctgry.0dihydrocodeine -0.162840
## elevmeds_ctgry.0dipyridamole+aspirin -0.202546
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.108466
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.150081
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.121296
## elevmeds_ctgry.0ephedrine -0.129630
## elevmeds_ctgry.0epinephrine -0.307407
## elevmeds_ctgry.0epoetin beta 0.237037
## elevmeds_ctgry.0etodolac 0.056400
## elevmeds_ctgry.0etoricoxib 0.062174
## elevmeds_ctgry.0felbinac 0.137037
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.427513
## elevmeds_ctgry.0florinef 100mcg tablet -0.412963
## elevmeds_ctgry.0fludrocortisone 0.127315
## elevmeds_ctgry.0flurbiprofen 0.067037
## elevmeds_ctgry.0froben 50mg tablet -1.146296
## elevmeds_ctgry.0gabapentin -0.110814
## elevmeds_ctgry.0goserelin 0.244729
## elevmeds_ctgry.0growth hormone product 0.150132
## elevmeds_ctgry.0hydrocortisone 0.066262
## elevmeds_ctgry.0hydrocortisone product 0.142122
## elevmeds_ctgry.0hydrocortisone+clotrimazole -0.104630
## elevmeds_ctgry.0hydrocortisone+lidocaine -0.078114
## elevmeds_ctgry.0hydrocortisone+miconazole -0.037037
## elevmeds_ctgry.0ibuprofen 0.103149
## elevmeds_ctgry.0ibuprofen product -0.170370
## elevmeds_ctgry.0indomethacin 0.185926
## elevmeds_ctgry.0indomethacin product 0.237037
## elevmeds_ctgry.0ketoprofen 0.184815
## elevmeds_ctgry.0ketotifen 0.014815
## elevmeds_ctgry.0lamictal 25mg tablet -0.027963
## elevmeds_ctgry.0liothyronine 0.166481
## elevmeds_ctgry.0lithium product -0.110037
## elevmeds_ctgry.0lyrica 25mg capsule -0.044781
## elevmeds_ctgry.0meloxicam 0.117602
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.116296
## elevmeds_ctgry.0methylprednisolone -0.190741
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.062963
## elevmeds_ctgry.0migril tablet 0.205664
## elevmeds_ctgry.0mobic 15mg tablet 0.056481
## elevmeds_ctgry.0mobic 7.5mg tablet -0.246296
## elevmeds_ctgry.0modafinil -0.094444
## elevmeds_ctgry.0nabumetone 0.174656
## elevmeds_ctgry.0napratec tablet combination pack 0.070370
## elevmeds_ctgry.0naprosyn 250mg tablet 0.082619
## elevmeds_ctgry.0naproxen 0.134422
## elevmeds_ctgry.0neurontin 100mg capsule 0.032870
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent -0.012963
## elevmeds_ctgry.0nuelin 125mg tablet -0.333333
## elevmeds_ctgry.0nurofen 200mg tablet 0.049074
## elevmeds_ctgry.0orphenadrine 0.327513
## elevmeds_ctgry.0oruvail 100 m/r capsule -0.274074
## elevmeds_ctgry.0oxcarbazepine -0.107407
## elevmeds_ctgry.0piroxicam 0.216085
## elevmeds_ctgry.0ponstan 250mg capsule 0.190370
## elevmeds_ctgry.0prednesol 5mg tablet 0.462037
## elevmeds_ctgry.0prednisolone 0.079002
## elevmeds_ctgry.0prednisolone product 0.064815
## elevmeds_ctgry.0prednisone 0.086388
## elevmeds_ctgry.0priadel 200mg m/r tablet -0.048828
## elevmeds_ctgry.0provigil 100mg tablet 0.375926
## elevmeds_ctgry.0pseudoephedrine 0.205787
## elevmeds_ctgry.0salicylic acid product 0.570370
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.077037
## elevmeds_ctgry.0sodium thyroxine 0.193350
## elevmeds_ctgry.0sodium valproate -0.054412
## elevmeds_ctgry.0somatropin 1.070370
## elevmeds_ctgry.0surgam 200mg tablet -0.462963
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment -1.096296
## elevmeds_ctgry.0tenoxicam 0.045370
## elevmeds_ctgry.0theophylline product -0.026599
## elevmeds_ctgry.0thyroxine product 0.214237
## elevmeds_ctgry.0thyroxine sodium 0.229900
## elevmeds_ctgry.0triamcinolone 0.091204
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.285522
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.314815
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.737037
## Std. Error
## (Intercept) 0.141097
## elevmeds_ctgry.0acemetacin 0.330901
## elevmeds_ctgry.0adrenaline product 0.199541
## elevmeds_ctgry.0amphotericin 0.302619
## elevmeds_ctgry.0anadin tablet 0.152107
## elevmeds_ctgry.0arcoxia 120mg tablet 0.183551
## elevmeds_ctgry.0arcoxia 60mg tablet 0.157324
## elevmeds_ctgry.0arcoxia 90mg tablet 0.176371
## elevmeds_ctgry.0arthrotec 50 tablet 0.149773
## elevmeds_ctgry.0arthrotec tablet 0.146992
## elevmeds_ctgry.0aspav dispersible tablet 0.244387
## elevmeds_ctgry.0aspirin 0.141158
## elevmeds_ctgry.0aspirin 75mg tablet 0.142662
## elevmeds_ctgry.0brufen 200mg tablet 0.236100
## elevmeds_ctgry.0carbamazepine 0.146965
## elevmeds_ctgry.0carbamazepine product 0.229097
## elevmeds_ctgry.0celebrex 100mg capsule 0.152013
## elevmeds_ctgry.0celebrex 200mg capsule 0.164118
## elevmeds_ctgry.0co-phenotrope 0.205682
## elevmeds_ctgry.0cortisone 0.199541
## elevmeds_ctgry.0cuprofen 200mg tablet 0.229097
## elevmeds_ctgry.0cya - cyclosporin 0.615026
## elevmeds_ctgry.0desmopressin product 0.446187
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.302619
## elevmeds_ctgry.0dexamethasone 0.170578
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.158435
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.148683
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.202454
## elevmeds_ctgry.0dihydrocodeine 0.144198
## elevmeds_ctgry.0dipyridamole+aspirin 0.254366
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.266648
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.188384
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.330901
## elevmeds_ctgry.0ephedrine 0.373307
## elevmeds_ctgry.0epinephrine 0.244387
## elevmeds_ctgry.0epoetin beta 0.615026
## elevmeds_ctgry.0etodolac 0.158676
## elevmeds_ctgry.0etoricoxib 0.151147
## elevmeds_ctgry.0felbinac 0.282193
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.266648
## elevmeds_ctgry.0florinef 100mcg tablet 0.446187
## elevmeds_ctgry.0fludrocortisone 0.165450
## elevmeds_ctgry.0flurbiprofen 0.236100
## elevmeds_ctgry.0froben 50mg tablet 0.446187
## elevmeds_ctgry.0gabapentin 0.143079
## elevmeds_ctgry.0goserelin 0.217884
## elevmeds_ctgry.0growth hormone product 0.213318
## elevmeds_ctgry.0hydrocortisone 0.146884
## elevmeds_ctgry.0hydrocortisone product 0.161189
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.254366
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.229097
## elevmeds_ctgry.0hydrocortisone+miconazole 0.244387
## elevmeds_ctgry.0ibuprofen 0.141164
## elevmeds_ctgry.0ibuprofen product 0.244387
## elevmeds_ctgry.0indomethacin 0.160875
## elevmeds_ctgry.0indomethacin product 0.446187
## elevmeds_ctgry.0ketoprofen 0.178475
## elevmeds_ctgry.0ketotifen 0.282193
## elevmeds_ctgry.0lamictal 25mg tablet 0.194488
## elevmeds_ctgry.0liothyronine 0.178475
## elevmeds_ctgry.0lithium product 0.150478
## elevmeds_ctgry.0lyrica 25mg capsule 0.175406
## elevmeds_ctgry.0meloxicam 0.145338
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.302619
## elevmeds_ctgry.0methylprednisolone 0.282193
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.615026
## elevmeds_ctgry.0migril tablet 0.202454
## elevmeds_ctgry.0mobic 15mg tablet 0.223093
## elevmeds_ctgry.0mobic 7.5mg tablet 0.330901
## elevmeds_ctgry.0modafinil 0.199541
## elevmeds_ctgry.0nabumetone 0.173628
## elevmeds_ctgry.0napratec tablet combination pack 0.330901
## elevmeds_ctgry.0naprosyn 250mg tablet 0.155646
## elevmeds_ctgry.0naproxen 0.142576
## elevmeds_ctgry.0neurontin 100mg capsule 0.254366
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.446187
## elevmeds_ctgry.0nuelin 125mg tablet 0.244387
## elevmeds_ctgry.0nurofen 200mg tablet 0.199541
## elevmeds_ctgry.0orphenadrine 0.266648
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.373307
## elevmeds_ctgry.0oxcarbazepine 0.223093
## elevmeds_ctgry.0piroxicam 0.173628
## elevmeds_ctgry.0ponstan 250mg capsule 0.302619
## elevmeds_ctgry.0prednesol 5mg tablet 0.330901
## elevmeds_ctgry.0prednisolone 0.142902
## elevmeds_ctgry.0prednisolone product 0.172807
## elevmeds_ctgry.0prednisone 0.156723
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.156347
## elevmeds_ctgry.0provigil 100mg tablet 0.282193
## elevmeds_ctgry.0pseudoephedrine 0.205682
## elevmeds_ctgry.0salicylic acid product 0.615026
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.302619
## elevmeds_ctgry.0sodium thyroxine 0.159178
## elevmeds_ctgry.0sodium valproate 0.151738
## elevmeds_ctgry.0somatropin 0.615026
## elevmeds_ctgry.0surgam 200mg tablet 0.446187
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.615026
## elevmeds_ctgry.0tenoxicam 0.330901
## elevmeds_ctgry.0theophylline product 0.229097
## elevmeds_ctgry.0thyroxine product 0.142065
## elevmeds_ctgry.0thyroxine sodium 0.143960
## elevmeds_ctgry.0triamcinolone 0.176371
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.229097
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.244387
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.615026
## t value
## (Intercept) 30.213
## elevmeds_ctgry.0acemetacin 1.220
## elevmeds_ctgry.0adrenaline product 0.278
## elevmeds_ctgry.0amphotericin -2.852
## elevmeds_ctgry.0anadin tablet 1.045
## elevmeds_ctgry.0arcoxia 120mg tablet 0.216
## elevmeds_ctgry.0arcoxia 60mg tablet 0.767
## elevmeds_ctgry.0arcoxia 90mg tablet -0.138
## elevmeds_ctgry.0arthrotec 50 tablet 1.024
## elevmeds_ctgry.0arthrotec tablet 0.537
## elevmeds_ctgry.0aspav dispersible tablet -0.030
## elevmeds_ctgry.0aspirin 0.998
## elevmeds_ctgry.0aspirin 75mg tablet 1.155
## elevmeds_ctgry.0brufen 200mg tablet -0.789
## elevmeds_ctgry.0carbamazepine 0.511
## elevmeds_ctgry.0carbamazepine product -1.941
## elevmeds_ctgry.0celebrex 100mg capsule 1.070
## elevmeds_ctgry.0celebrex 200mg capsule 1.168
## elevmeds_ctgry.0co-phenotrope 0.717
## elevmeds_ctgry.0cortisone -0.051
## elevmeds_ctgry.0cuprofen 200mg tablet 1.511
## elevmeds_ctgry.0cya - cyclosporin -0.753
## elevmeds_ctgry.0desmopressin product 0.531
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.519
## elevmeds_ctgry.0dexamethasone -0.960
## elevmeds_ctgry.0diclofenac sodium+misoprostol -0.105
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.260
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -0.330
## elevmeds_ctgry.0dihydrocodeine -1.129
## elevmeds_ctgry.0dipyridamole+aspirin -0.796
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.407
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.797
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.367
## elevmeds_ctgry.0ephedrine -0.347
## elevmeds_ctgry.0epinephrine -1.258
## elevmeds_ctgry.0epoetin beta 0.385
## elevmeds_ctgry.0etodolac 0.355
## elevmeds_ctgry.0etoricoxib 0.411
## elevmeds_ctgry.0felbinac 0.486
## elevmeds_ctgry.0fenactol 25mg e/c tablet 1.603
## elevmeds_ctgry.0florinef 100mcg tablet -0.926
## elevmeds_ctgry.0fludrocortisone 0.770
## elevmeds_ctgry.0flurbiprofen 0.284
## elevmeds_ctgry.0froben 50mg tablet -2.569
## elevmeds_ctgry.0gabapentin -0.774
## elevmeds_ctgry.0goserelin 1.123
## elevmeds_ctgry.0growth hormone product 0.704
## elevmeds_ctgry.0hydrocortisone 0.451
## elevmeds_ctgry.0hydrocortisone product 0.882
## elevmeds_ctgry.0hydrocortisone+clotrimazole -0.411
## elevmeds_ctgry.0hydrocortisone+lidocaine -0.341
## elevmeds_ctgry.0hydrocortisone+miconazole -0.152
## elevmeds_ctgry.0ibuprofen 0.731
## elevmeds_ctgry.0ibuprofen product -0.697
## elevmeds_ctgry.0indomethacin 1.156
## elevmeds_ctgry.0indomethacin product 0.531
## elevmeds_ctgry.0ketoprofen 1.036
## elevmeds_ctgry.0ketotifen 0.052
## elevmeds_ctgry.0lamictal 25mg tablet -0.144
## elevmeds_ctgry.0liothyronine 0.933
## elevmeds_ctgry.0lithium product -0.731
## elevmeds_ctgry.0lyrica 25mg capsule -0.255
## elevmeds_ctgry.0meloxicam 0.809
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.384
## elevmeds_ctgry.0methylprednisolone -0.676
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.102
## elevmeds_ctgry.0migril tablet 1.016
## elevmeds_ctgry.0mobic 15mg tablet 0.253
## elevmeds_ctgry.0mobic 7.5mg tablet -0.744
## elevmeds_ctgry.0modafinil -0.473
## elevmeds_ctgry.0nabumetone 1.006
## elevmeds_ctgry.0napratec tablet combination pack 0.213
## elevmeds_ctgry.0naprosyn 250mg tablet 0.531
## elevmeds_ctgry.0naproxen 0.943
## elevmeds_ctgry.0neurontin 100mg capsule 0.129
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent -0.029
## elevmeds_ctgry.0nuelin 125mg tablet -1.364
## elevmeds_ctgry.0nurofen 200mg tablet 0.246
## elevmeds_ctgry.0orphenadrine 1.228
## elevmeds_ctgry.0oruvail 100 m/r capsule -0.734
## elevmeds_ctgry.0oxcarbazepine -0.481
## elevmeds_ctgry.0piroxicam 1.245
## elevmeds_ctgry.0ponstan 250mg capsule 0.629
## elevmeds_ctgry.0prednesol 5mg tablet 1.396
## elevmeds_ctgry.0prednisolone 0.553
## elevmeds_ctgry.0prednisolone product 0.375
## elevmeds_ctgry.0prednisone 0.551
## elevmeds_ctgry.0priadel 200mg m/r tablet -0.312
## elevmeds_ctgry.0provigil 100mg tablet 1.332
## elevmeds_ctgry.0pseudoephedrine 1.001
## elevmeds_ctgry.0salicylic acid product 0.927
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.255
## elevmeds_ctgry.0sodium thyroxine 1.215
## elevmeds_ctgry.0sodium valproate -0.359
## elevmeds_ctgry.0somatropin 1.740
## elevmeds_ctgry.0surgam 200mg tablet -1.038
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment -1.783
## elevmeds_ctgry.0tenoxicam 0.137
## elevmeds_ctgry.0theophylline product -0.116
## elevmeds_ctgry.0thyroxine product 1.508
## elevmeds_ctgry.0thyroxine sodium 1.597
## elevmeds_ctgry.0triamcinolone 0.517
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 1.246
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 1.288
## elevmeds_ctgry.0voltarene 25mg e/c tablet 1.198
## Pr(>|t|)
## (Intercept) < 2e-16
## elevmeds_ctgry.0acemetacin 0.22247
## elevmeds_ctgry.0adrenaline product 0.78069
## elevmeds_ctgry.0amphotericin 0.00435
## elevmeds_ctgry.0anadin tablet 0.29601
## elevmeds_ctgry.0arcoxia 120mg tablet 0.82918
## elevmeds_ctgry.0arcoxia 60mg tablet 0.44336
## elevmeds_ctgry.0arcoxia 90mg tablet 0.88987
## elevmeds_ctgry.0arthrotec 50 tablet 0.30589
## elevmeds_ctgry.0arthrotec tablet 0.59106
## elevmeds_ctgry.0aspav dispersible tablet 0.97582
## elevmeds_ctgry.0aspirin 0.31830
## elevmeds_ctgry.0aspirin 75mg tablet 0.24798
## elevmeds_ctgry.0brufen 200mg tablet 0.43008
## elevmeds_ctgry.0carbamazepine 0.60941
## elevmeds_ctgry.0carbamazepine product 0.05221
## elevmeds_ctgry.0celebrex 100mg capsule 0.28469
## elevmeds_ctgry.0celebrex 200mg capsule 0.24300
## elevmeds_ctgry.0co-phenotrope 0.47344
## elevmeds_ctgry.0cortisone 0.95929
## elevmeds_ctgry.0cuprofen 200mg tablet 0.13084
## elevmeds_ctgry.0cya - cyclosporin 0.45160
## elevmeds_ctgry.0desmopressin product 0.59525
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.60381
## elevmeds_ctgry.0dexamethasone 0.33687
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.91662
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.79479
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.74112
## elevmeds_ctgry.0dihydrocodeine 0.25879
## elevmeds_ctgry.0dipyridamole+aspirin 0.42587
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.68417
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.42565
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.71395
## elevmeds_ctgry.0ephedrine 0.72841
## elevmeds_ctgry.0epinephrine 0.20844
## elevmeds_ctgry.0epoetin beta 0.69994
## elevmeds_ctgry.0etodolac 0.72226
## elevmeds_ctgry.0etoricoxib 0.68082
## elevmeds_ctgry.0felbinac 0.62724
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.10888
## elevmeds_ctgry.0florinef 100mcg tablet 0.35469
## elevmeds_ctgry.0fludrocortisone 0.44160
## elevmeds_ctgry.0flurbiprofen 0.77646
## elevmeds_ctgry.0froben 50mg tablet 0.01020
## elevmeds_ctgry.0gabapentin 0.43864
## elevmeds_ctgry.0goserelin 0.26135
## elevmeds_ctgry.0growth hormone product 0.48156
## elevmeds_ctgry.0hydrocortisone 0.65191
## elevmeds_ctgry.0hydrocortisone product 0.37794
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.68083
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.73313
## elevmeds_ctgry.0hydrocortisone+miconazole 0.87954
## elevmeds_ctgry.0ibuprofen 0.46497
## elevmeds_ctgry.0ibuprofen product 0.48572
## elevmeds_ctgry.0indomethacin 0.24780
## elevmeds_ctgry.0indomethacin product 0.59525
## elevmeds_ctgry.0ketoprofen 0.30043
## elevmeds_ctgry.0ketotifen 0.95813
## elevmeds_ctgry.0lamictal 25mg tablet 0.88568
## elevmeds_ctgry.0liothyronine 0.35093
## elevmeds_ctgry.0lithium product 0.46463
## elevmeds_ctgry.0lyrica 25mg capsule 0.79849
## elevmeds_ctgry.0meloxicam 0.41842
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.70076
## elevmeds_ctgry.0methylprednisolone 0.49909
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.91846
## elevmeds_ctgry.0migril tablet 0.30970
## elevmeds_ctgry.0mobic 15mg tablet 0.80013
## elevmeds_ctgry.0mobic 7.5mg tablet 0.45669
## elevmeds_ctgry.0modafinil 0.63599
## elevmeds_ctgry.0nabumetone 0.31446
## elevmeds_ctgry.0napratec tablet combination pack 0.83159
## elevmeds_ctgry.0naprosyn 250mg tablet 0.59555
## elevmeds_ctgry.0naproxen 0.34578
## elevmeds_ctgry.0neurontin 100mg capsule 0.89718
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.97682
## elevmeds_ctgry.0nuelin 125mg tablet 0.17259
## elevmeds_ctgry.0nurofen 200mg tablet 0.80573
## elevmeds_ctgry.0orphenadrine 0.21935
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.46284
## elevmeds_ctgry.0oxcarbazepine 0.63020
## elevmeds_ctgry.0piroxicam 0.21331
## elevmeds_ctgry.0ponstan 250mg capsule 0.52930
## elevmeds_ctgry.0prednesol 5mg tablet 0.16263
## elevmeds_ctgry.0prednisolone 0.58038
## elevmeds_ctgry.0prednisolone product 0.70761
## elevmeds_ctgry.0prednisone 0.58149
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.75481
## elevmeds_ctgry.0provigil 100mg tablet 0.18281
## elevmeds_ctgry.0pseudoephedrine 0.31707
## elevmeds_ctgry.0salicylic acid product 0.35373
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.79906
## elevmeds_ctgry.0sodium thyroxine 0.22450
## elevmeds_ctgry.0sodium valproate 0.71990
## elevmeds_ctgry.0somatropin 0.08180
## elevmeds_ctgry.0surgam 200mg tablet 0.29946
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.07467
## elevmeds_ctgry.0tenoxicam 0.89094
## elevmeds_ctgry.0theophylline product 0.90757
## elevmeds_ctgry.0thyroxine product 0.13156
## elevmeds_ctgry.0thyroxine sodium 0.11028
## elevmeds_ctgry.0triamcinolone 0.60508
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.21266
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.19769
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.23077
##
## (Intercept) ***
## elevmeds_ctgry.0acemetacin
## elevmeds_ctgry.0adrenaline product
## elevmeds_ctgry.0amphotericin **
## elevmeds_ctgry.0anadin tablet
## elevmeds_ctgry.0arcoxia 120mg tablet
## elevmeds_ctgry.0arcoxia 60mg tablet
## elevmeds_ctgry.0arcoxia 90mg tablet
## elevmeds_ctgry.0arthrotec 50 tablet
## elevmeds_ctgry.0arthrotec tablet
## elevmeds_ctgry.0aspav dispersible tablet
## elevmeds_ctgry.0aspirin
## elevmeds_ctgry.0aspirin 75mg tablet
## elevmeds_ctgry.0brufen 200mg tablet
## elevmeds_ctgry.0carbamazepine
## elevmeds_ctgry.0carbamazepine product .
## elevmeds_ctgry.0celebrex 100mg capsule
## elevmeds_ctgry.0celebrex 200mg capsule
## elevmeds_ctgry.0co-phenotrope
## elevmeds_ctgry.0cortisone
## elevmeds_ctgry.0cuprofen 200mg tablet
## elevmeds_ctgry.0cya - cyclosporin
## elevmeds_ctgry.0desmopressin product
## elevmeds_ctgry.0desmospray 10micrograms nasal spray
## elevmeds_ctgry.0dexamethasone
## elevmeds_ctgry.0diclofenac sodium+misoprostol
## elevmeds_ctgry.0dicloflex 25mg e/c tablet
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule
## elevmeds_ctgry.0dihydrocodeine
## elevmeds_ctgry.0dipyridamole+aspirin
## elevmeds_ctgry.0eccoxolac 300mg capsule
## elevmeds_ctgry.0eltroxin 25micrograms tablet
## elevmeds_ctgry.0entocort cr 3mg m/r capsule
## elevmeds_ctgry.0ephedrine
## elevmeds_ctgry.0epinephrine
## elevmeds_ctgry.0epoetin beta
## elevmeds_ctgry.0etodolac
## elevmeds_ctgry.0etoricoxib
## elevmeds_ctgry.0felbinac
## elevmeds_ctgry.0fenactol 25mg e/c tablet
## elevmeds_ctgry.0florinef 100mcg tablet
## elevmeds_ctgry.0fludrocortisone
## elevmeds_ctgry.0flurbiprofen
## elevmeds_ctgry.0froben 50mg tablet *
## elevmeds_ctgry.0gabapentin
## elevmeds_ctgry.0goserelin
## elevmeds_ctgry.0growth hormone product
## elevmeds_ctgry.0hydrocortisone
## elevmeds_ctgry.0hydrocortisone product
## elevmeds_ctgry.0hydrocortisone+clotrimazole
## elevmeds_ctgry.0hydrocortisone+lidocaine
## elevmeds_ctgry.0hydrocortisone+miconazole
## elevmeds_ctgry.0ibuprofen
## elevmeds_ctgry.0ibuprofen product
## elevmeds_ctgry.0indomethacin
## elevmeds_ctgry.0indomethacin product
## elevmeds_ctgry.0ketoprofen
## elevmeds_ctgry.0ketotifen
## elevmeds_ctgry.0lamictal 25mg tablet
## elevmeds_ctgry.0liothyronine
## elevmeds_ctgry.0lithium product
## elevmeds_ctgry.0lyrica 25mg capsule
## elevmeds_ctgry.0meloxicam
## elevmeds_ctgry.0mesren mr 400mg m/r tablet
## elevmeds_ctgry.0methylprednisolone
## elevmeds_ctgry.0micropirin 75mg e/c tablet
## elevmeds_ctgry.0migril tablet
## elevmeds_ctgry.0mobic 15mg tablet
## elevmeds_ctgry.0mobic 7.5mg tablet
## elevmeds_ctgry.0modafinil
## elevmeds_ctgry.0nabumetone
## elevmeds_ctgry.0napratec tablet combination pack
## elevmeds_ctgry.0naprosyn 250mg tablet
## elevmeds_ctgry.0naproxen
## elevmeds_ctgry.0neurontin 100mg capsule
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent
## elevmeds_ctgry.0nuelin 125mg tablet
## elevmeds_ctgry.0nurofen 200mg tablet
## elevmeds_ctgry.0orphenadrine
## elevmeds_ctgry.0oruvail 100 m/r capsule
## elevmeds_ctgry.0oxcarbazepine
## elevmeds_ctgry.0piroxicam
## elevmeds_ctgry.0ponstan 250mg capsule
## elevmeds_ctgry.0prednesol 5mg tablet
## elevmeds_ctgry.0prednisolone
## elevmeds_ctgry.0prednisolone product
## elevmeds_ctgry.0prednisone
## elevmeds_ctgry.0priadel 200mg m/r tablet
## elevmeds_ctgry.0provigil 100mg tablet
## elevmeds_ctgry.0pseudoephedrine
## elevmeds_ctgry.0salicylic acid product
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule
## elevmeds_ctgry.0sodium thyroxine
## elevmeds_ctgry.0sodium valproate
## elevmeds_ctgry.0somatropin .
## elevmeds_ctgry.0surgam 200mg tablet
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment .
## elevmeds_ctgry.0tenoxicam
## elevmeds_ctgry.0theophylline product
## elevmeds_ctgry.0thyroxine product
## elevmeds_ctgry.0thyroxine sodium
## elevmeds_ctgry.0triamcinolone
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet
## elevmeds_ctgry.0voltarene 25mg e/c tablet
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5986 on 47782 degrees of freedom
## (454607 observations deleted due to missingness)
## Multiple R-squared: 0.009133, Adjusted R-squared: 0.006976
## F-statistic: 4.235 on 104 and 47782 DF, p-value: < 2.2e-16# with covariates
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit888 <- lm(mdl, data=dat)
summary(mdl_fit888)##
## Call:
## lm(formula = mdl, data = dat)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.5776, -0.3496, 0.0157, 0.3761, 2.3436
##
## Coefficients:
## Estimate
## (Intercept) 3.9251654
## elevmeds_ctgry.0acemetacin 0.4329500
## elevmeds_ctgry.0adrenaline product 0.1784494
## elevmeds_ctgry.0amphotericin -0.1858484
## elevmeds_ctgry.0anadin tablet 0.1702531
## elevmeds_ctgry.0arcoxia 120mg tablet 0.0343089
## elevmeds_ctgry.0arcoxia 60mg tablet 0.0662528
## elevmeds_ctgry.0arcoxia 90mg tablet -0.0666337
## elevmeds_ctgry.0arthrotec 50 tablet 0.1031643
## elevmeds_ctgry.0arthrotec tablet 0.0123968
## elevmeds_ctgry.0aspav dispersible tablet -0.0438845
## elevmeds_ctgry.0aspirin 0.1064466
## elevmeds_ctgry.0aspirin 75mg tablet 0.1153584
## elevmeds_ctgry.0brufen 200mg tablet -0.2073254
## elevmeds_ctgry.0carbamazepine 0.0623665
## elevmeds_ctgry.0carbamazepine product -0.4269784
## elevmeds_ctgry.0celebrex 100mg capsule 0.1086711
## elevmeds_ctgry.0celebrex 200mg capsule 0.0887282
## elevmeds_ctgry.0co-phenotrope 0.0594094
## elevmeds_ctgry.0cortisone -0.0768881
## elevmeds_ctgry.0cuprofen 200mg tablet 0.3533558
## elevmeds_ctgry.0cya - cyclosporin -0.4883922
## elevmeds_ctgry.0desmopressin product 0.1345339
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.1895112
## elevmeds_ctgry.0dexamethasone -0.1448995
## elevmeds_ctgry.0diclofenac sodium+misoprostol -0.0457314
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.0457814
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -0.2008786
## elevmeds_ctgry.0dihydrocodeine -0.1240662
## elevmeds_ctgry.0dipyridamole+aspirin -0.4496358
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.0922828
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.1518964
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.1755594
## elevmeds_ctgry.0ephedrine 0.2216372
## elevmeds_ctgry.0epinephrine -0.2524656
## elevmeds_ctgry.0epoetin beta -0.0970741
## elevmeds_ctgry.0etodolac 0.0292190
## elevmeds_ctgry.0etoricoxib 0.0899653
## elevmeds_ctgry.0felbinac 0.1044355
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.2198032
## elevmeds_ctgry.0florinef 100mcg tablet -0.5730110
## elevmeds_ctgry.0fludrocortisone 0.1153598
## elevmeds_ctgry.0flurbiprofen 0.0801409
## elevmeds_ctgry.0froben 50mg tablet -0.8180368
## elevmeds_ctgry.0gabapentin -0.0797384
## elevmeds_ctgry.0goserelin 0.1578716
## elevmeds_ctgry.0growth hormone product 0.0831600
## elevmeds_ctgry.0hydrocortisone 0.0627609
## elevmeds_ctgry.0hydrocortisone product 0.1206518
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.0333957
## elevmeds_ctgry.0hydrocortisone+lidocaine -0.0363588
## elevmeds_ctgry.0hydrocortisone+miconazole -0.0797430
## elevmeds_ctgry.0ibuprofen 0.0868934
## elevmeds_ctgry.0ibuprofen product -0.2314265
## elevmeds_ctgry.0indomethacin 0.1461981
## elevmeds_ctgry.0indomethacin product 0.3046531
## elevmeds_ctgry.0ketoprofen 0.1024733
## elevmeds_ctgry.0ketotifen 0.0203066
## elevmeds_ctgry.0lamictal 25mg tablet -0.0002988
## elevmeds_ctgry.0liothyronine 0.1277084
## elevmeds_ctgry.0lithium product -0.0294765
## elevmeds_ctgry.0lyrica 25mg capsule -0.0564899
## elevmeds_ctgry.0meloxicam 0.0532045
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.1840643
## elevmeds_ctgry.0methylprednisolone -0.2112685
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.0590250
## elevmeds_ctgry.0migril tablet 0.0987295
## elevmeds_ctgry.0mobic 15mg tablet -0.0287918
## elevmeds_ctgry.0mobic 7.5mg tablet -0.3118341
## elevmeds_ctgry.0modafinil -0.0544318
## elevmeds_ctgry.0nabumetone 0.1279060
## elevmeds_ctgry.0napratec tablet combination pack 0.0459506
## elevmeds_ctgry.0naprosyn 250mg tablet 0.0230198
## elevmeds_ctgry.0naproxen 0.0846738
## elevmeds_ctgry.0neurontin 100mg capsule -0.0783687
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent -0.0287558
## elevmeds_ctgry.0nuelin 125mg tablet -0.4019382
## elevmeds_ctgry.0nurofen 200mg tablet 0.0277237
## elevmeds_ctgry.0orphenadrine 0.3543142
## elevmeds_ctgry.0oruvail 100 m/r capsule -0.4256068
## elevmeds_ctgry.0oxcarbazepine -0.1708846
## elevmeds_ctgry.0piroxicam 0.1678923
## elevmeds_ctgry.0ponstan 250mg capsule 0.1952517
## elevmeds_ctgry.0prednesol 5mg tablet 0.2985054
## elevmeds_ctgry.0prednisolone 0.0208911
## elevmeds_ctgry.0prednisolone product 0.0219084
## elevmeds_ctgry.0prednisone 0.0003935
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.0363668
## elevmeds_ctgry.0provigil 100mg tablet 0.3195126
## elevmeds_ctgry.0pseudoephedrine 0.1279910
## elevmeds_ctgry.0salicylic acid product 0.4268761
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.1910909
## elevmeds_ctgry.0sodium thyroxine 0.1475587
## elevmeds_ctgry.0sodium valproate -0.0397205
## elevmeds_ctgry.0somatropin 1.3086004
## elevmeds_ctgry.0surgam 200mg tablet -0.6232578
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment -1.0376178
## elevmeds_ctgry.0tenoxicam 0.0843999
## elevmeds_ctgry.0theophylline product -0.0071802
## elevmeds_ctgry.0thyroxine product 0.1609168
## elevmeds_ctgry.0thyroxine sodium 0.1749899
## elevmeds_ctgry.0triamcinolone 0.1051456
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.2017268
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.1941344
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.6695744
## mean_sbp.0 0.0016159
## high_bp.0 -0.0659702
## htn_meds_count.0 0.0025224
## f.21003.0.0 0.0154346
## f.31.0.0 -0.0224583
## angina.0 -0.1180515
## heartattack.0 -0.0752541
## f.2443.0.0 -0.0722984
## depr_l.0 -0.3998695
## f.21001.0.0 -0.0088383
## mean_hr.0 -0.0027000
## Std. Error
## (Intercept) 0.1454316
## elevmeds_ctgry.0acemetacin 0.3183487
## elevmeds_ctgry.0adrenaline product 0.1937603
## elevmeds_ctgry.0amphotericin 0.3588202
## elevmeds_ctgry.0anadin tablet 0.1506427
## elevmeds_ctgry.0arcoxia 120mg tablet 0.1786909
## elevmeds_ctgry.0arcoxia 60mg tablet 0.1546926
## elevmeds_ctgry.0arcoxia 90mg tablet 0.1719604
## elevmeds_ctgry.0arthrotec 50 tablet 0.1476190
## elevmeds_ctgry.0arthrotec tablet 0.1447316
## elevmeds_ctgry.0aspav dispersible tablet 0.2361938
## elevmeds_ctgry.0aspirin 0.1390360
## elevmeds_ctgry.0aspirin 75mg tablet 0.1404974
## elevmeds_ctgry.0brufen 200mg tablet 0.2456479
## elevmeds_ctgry.0carbamazepine 0.1446249
## elevmeds_ctgry.0carbamazepine product 0.2217123
## elevmeds_ctgry.0celebrex 100mg capsule 0.1496743
## elevmeds_ctgry.0celebrex 200mg capsule 0.1621508
## elevmeds_ctgry.0co-phenotrope 0.2029342
## elevmeds_ctgry.0cortisone 0.2029442
## elevmeds_ctgry.0cuprofen 200mg tablet 0.2217036
## elevmeds_ctgry.0cya - cyclosporin 0.5894824
## elevmeds_ctgry.0desmopressin product 0.4282683
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.2914649
## elevmeds_ctgry.0dexamethasone 0.1739223
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.1558193
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.1463784
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.1965048
## elevmeds_ctgry.0dihydrocodeine 0.1420511
## elevmeds_ctgry.0dipyridamole+aspirin 0.2720345
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.2572588
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.1850242
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.3183711
## elevmeds_ctgry.0ephedrine 0.5894990
## elevmeds_ctgry.0epinephrine 0.2361965
## elevmeds_ctgry.0epoetin beta 0.5894603
## elevmeds_ctgry.0etodolac 0.1568345
## elevmeds_ctgry.0etoricoxib 0.1487819
## elevmeds_ctgry.0felbinac 0.2720542
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.2573069
## elevmeds_ctgry.0florinef 100mcg tablet 0.4282424
## elevmeds_ctgry.0fludrocortisone 0.1617120
## elevmeds_ctgry.0flurbiprofen 0.2283089
## elevmeds_ctgry.0froben 50mg tablet 0.4282944
## elevmeds_ctgry.0gabapentin 0.1409702
## elevmeds_ctgry.0goserelin 0.2283259
## elevmeds_ctgry.0growth hormone product 0.2160103
## elevmeds_ctgry.0hydrocortisone 0.1445563
## elevmeds_ctgry.0hydrocortisone product 0.1583402
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.2573007
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.2283048
## elevmeds_ctgry.0hydrocortisone+miconazole 0.2456521
## elevmeds_ctgry.0ibuprofen 0.1390567
## elevmeds_ctgry.0ibuprofen product 0.2456040
## elevmeds_ctgry.0indomethacin 0.1586710
## elevmeds_ctgry.0indomethacin product 0.4282898
## elevmeds_ctgry.0ketoprofen 0.1761535
## elevmeds_ctgry.0ketotifen 0.2914829
## elevmeds_ctgry.0lamictal 25mg tablet 0.1912671
## elevmeds_ctgry.0liothyronine 0.1761590
## elevmeds_ctgry.0lithium product 0.1480381
## elevmeds_ctgry.0lyrica 25mg capsule 0.1739127
## elevmeds_ctgry.0meloxicam 0.1429343
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.2914676
## elevmeds_ctgry.0methylprednisolone 0.2720687
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.5896264
## elevmeds_ctgry.0migril tablet 0.1964997
## elevmeds_ctgry.0mobic 15mg tablet 0.2159885
## elevmeds_ctgry.0mobic 7.5mg tablet 0.3183547
## elevmeds_ctgry.0modafinil 0.1937596
## elevmeds_ctgry.0nabumetone 0.1693523
## elevmeds_ctgry.0napratec tablet combination pack 0.3183671
## elevmeds_ctgry.0naprosyn 250mg tablet 0.1542881
## elevmeds_ctgry.0naproxen 0.1404311
## elevmeds_ctgry.0neurontin 100mg capsule 0.2456240
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.4282453
## elevmeds_ctgry.0nuelin 125mg tablet 0.2361680
## elevmeds_ctgry.0nurofen 200mg tablet 0.1995543
## elevmeds_ctgry.0orphenadrine 0.2572971
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.3587547
## elevmeds_ctgry.0oxcarbazepine 0.2160193
## elevmeds_ctgry.0piroxicam 0.1719326
## elevmeds_ctgry.0ponstan 250mg capsule 0.2915054
## elevmeds_ctgry.0prednesol 5mg tablet 0.3183742
## elevmeds_ctgry.0prednisolone 0.1406800
## elevmeds_ctgry.0prednisolone product 0.1701720
## elevmeds_ctgry.0prednisone 0.1542913
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.1533592
## elevmeds_ctgry.0provigil 100mg tablet 0.2720371
## elevmeds_ctgry.0pseudoephedrine 0.2110773
## elevmeds_ctgry.0salicylic acid product 0.5895707
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.2914400
## elevmeds_ctgry.0sodium thyroxine 0.1563278
## elevmeds_ctgry.0sodium valproate 0.1490480
## elevmeds_ctgry.0somatropin 0.5895271
## elevmeds_ctgry.0surgam 200mg tablet 0.4282490
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.5894831
## elevmeds_ctgry.0tenoxicam 0.3183399
## elevmeds_ctgry.0theophylline product 0.2216758
## elevmeds_ctgry.0thyroxine product 0.1399022
## elevmeds_ctgry.0thyroxine sodium 0.1417003
## elevmeds_ctgry.0triamcinolone 0.1773777
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.2216726
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.2572667
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.5894755
## mean_sbp.0 0.0001686
## high_bp.0 0.0073988
## htn_meds_count.0 0.0035796
## f.21003.0.0 0.0004104
## f.31.0.0 0.0060566
## angina.0 0.0106551
## heartattack.0 0.0118141
## f.2443.0.0 0.0087950
## depr_l.0 0.0107643
## f.21001.0.0 0.0005826
## mean_hr.0 0.0002509
## t value
## (Intercept) 26.990
## elevmeds_ctgry.0acemetacin 1.360
## elevmeds_ctgry.0adrenaline product 0.921
## elevmeds_ctgry.0amphotericin -0.518
## elevmeds_ctgry.0anadin tablet 1.130
## elevmeds_ctgry.0arcoxia 120mg tablet 0.192
## elevmeds_ctgry.0arcoxia 60mg tablet 0.428
## elevmeds_ctgry.0arcoxia 90mg tablet -0.387
## elevmeds_ctgry.0arthrotec 50 tablet 0.699
## elevmeds_ctgry.0arthrotec tablet 0.086
## elevmeds_ctgry.0aspav dispersible tablet -0.186
## elevmeds_ctgry.0aspirin 0.766
## elevmeds_ctgry.0aspirin 75mg tablet 0.821
## elevmeds_ctgry.0brufen 200mg tablet -0.844
## elevmeds_ctgry.0carbamazepine 0.431
## elevmeds_ctgry.0carbamazepine product -1.926
## elevmeds_ctgry.0celebrex 100mg capsule 0.726
## elevmeds_ctgry.0celebrex 200mg capsule 0.547
## elevmeds_ctgry.0co-phenotrope 0.293
## elevmeds_ctgry.0cortisone -0.379
## elevmeds_ctgry.0cuprofen 200mg tablet 1.594
## elevmeds_ctgry.0cya - cyclosporin -0.829
## elevmeds_ctgry.0desmopressin product 0.314
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.650
## elevmeds_ctgry.0dexamethasone -0.833
## elevmeds_ctgry.0diclofenac sodium+misoprostol -0.293
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.313
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule -1.022
## elevmeds_ctgry.0dihydrocodeine -0.873
## elevmeds_ctgry.0dipyridamole+aspirin -1.653
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.359
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.821
## elevmeds_ctgry.0entocort cr 3mg m/r capsule -0.551
## elevmeds_ctgry.0ephedrine 0.376
## elevmeds_ctgry.0epinephrine -1.069
## elevmeds_ctgry.0epoetin beta -0.165
## elevmeds_ctgry.0etodolac 0.186
## elevmeds_ctgry.0etoricoxib 0.605
## elevmeds_ctgry.0felbinac 0.384
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.854
## elevmeds_ctgry.0florinef 100mcg tablet -1.338
## elevmeds_ctgry.0fludrocortisone 0.713
## elevmeds_ctgry.0flurbiprofen 0.351
## elevmeds_ctgry.0froben 50mg tablet -1.910
## elevmeds_ctgry.0gabapentin -0.566
## elevmeds_ctgry.0goserelin 0.691
## elevmeds_ctgry.0growth hormone product 0.385
## elevmeds_ctgry.0hydrocortisone 0.434
## elevmeds_ctgry.0hydrocortisone product 0.762
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.130
## elevmeds_ctgry.0hydrocortisone+lidocaine -0.159
## elevmeds_ctgry.0hydrocortisone+miconazole -0.325
## elevmeds_ctgry.0ibuprofen 0.625
## elevmeds_ctgry.0ibuprofen product -0.942
## elevmeds_ctgry.0indomethacin 0.921
## elevmeds_ctgry.0indomethacin product 0.711
## elevmeds_ctgry.0ketoprofen 0.582
## elevmeds_ctgry.0ketotifen 0.070
## elevmeds_ctgry.0lamictal 25mg tablet -0.002
## elevmeds_ctgry.0liothyronine 0.725
## elevmeds_ctgry.0lithium product -0.199
## elevmeds_ctgry.0lyrica 25mg capsule -0.325
## elevmeds_ctgry.0meloxicam 0.372
## elevmeds_ctgry.0mesren mr 400mg m/r tablet -0.632
## elevmeds_ctgry.0methylprednisolone -0.777
## elevmeds_ctgry.0micropirin 75mg e/c tablet -0.100
## elevmeds_ctgry.0migril tablet 0.502
## elevmeds_ctgry.0mobic 15mg tablet -0.133
## elevmeds_ctgry.0mobic 7.5mg tablet -0.980
## elevmeds_ctgry.0modafinil -0.281
## elevmeds_ctgry.0nabumetone 0.755
## elevmeds_ctgry.0napratec tablet combination pack 0.144
## elevmeds_ctgry.0naprosyn 250mg tablet 0.149
## elevmeds_ctgry.0naproxen 0.603
## elevmeds_ctgry.0neurontin 100mg capsule -0.319
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent -0.067
## elevmeds_ctgry.0nuelin 125mg tablet -1.702
## elevmeds_ctgry.0nurofen 200mg tablet 0.139
## elevmeds_ctgry.0orphenadrine 1.377
## elevmeds_ctgry.0oruvail 100 m/r capsule -1.186
## elevmeds_ctgry.0oxcarbazepine -0.791
## elevmeds_ctgry.0piroxicam 0.977
## elevmeds_ctgry.0ponstan 250mg capsule 0.670
## elevmeds_ctgry.0prednesol 5mg tablet 0.938
## elevmeds_ctgry.0prednisolone 0.149
## elevmeds_ctgry.0prednisolone product 0.129
## elevmeds_ctgry.0prednisone 0.003
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.237
## elevmeds_ctgry.0provigil 100mg tablet 1.175
## elevmeds_ctgry.0pseudoephedrine 0.606
## elevmeds_ctgry.0salicylic acid product 0.724
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.656
## elevmeds_ctgry.0sodium thyroxine 0.944
## elevmeds_ctgry.0sodium valproate -0.266
## elevmeds_ctgry.0somatropin 2.220
## elevmeds_ctgry.0surgam 200mg tablet -1.455
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment -1.760
## elevmeds_ctgry.0tenoxicam 0.265
## elevmeds_ctgry.0theophylline product -0.032
## elevmeds_ctgry.0thyroxine product 1.150
## elevmeds_ctgry.0thyroxine sodium 1.235
## elevmeds_ctgry.0triamcinolone 0.593
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.910
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.755
## elevmeds_ctgry.0voltarene 25mg e/c tablet 1.136
## mean_sbp.0 9.583
## high_bp.0 -8.916
## htn_meds_count.0 0.705
## f.21003.0.0 37.612
## f.31.0.0 -3.708
## angina.0 -11.079
## heartattack.0 -6.370
## f.2443.0.0 -8.220
## depr_l.0 -37.148
## f.21001.0.0 -15.170
## mean_hr.0 -10.763
## Pr(>|t|)
## (Intercept) < 2e-16
## elevmeds_ctgry.0acemetacin 0.173841
## elevmeds_ctgry.0adrenaline product 0.357066
## elevmeds_ctgry.0amphotericin 0.604501
## elevmeds_ctgry.0anadin tablet 0.258408
## elevmeds_ctgry.0arcoxia 120mg tablet 0.847742
## elevmeds_ctgry.0arcoxia 60mg tablet 0.668444
## elevmeds_ctgry.0arcoxia 90mg tablet 0.698392
## elevmeds_ctgry.0arthrotec 50 tablet 0.484647
## elevmeds_ctgry.0arthrotec tablet 0.931742
## elevmeds_ctgry.0aspav dispersible tablet 0.852604
## elevmeds_ctgry.0aspirin 0.443916
## elevmeds_ctgry.0aspirin 75mg tablet 0.411611
## elevmeds_ctgry.0brufen 200mg tablet 0.398677
## elevmeds_ctgry.0carbamazepine 0.666304
## elevmeds_ctgry.0carbamazepine product 0.054133
## elevmeds_ctgry.0celebrex 100mg capsule 0.467812
## elevmeds_ctgry.0celebrex 200mg capsule 0.584247
## elevmeds_ctgry.0co-phenotrope 0.769713
## elevmeds_ctgry.0cortisone 0.704791
## elevmeds_ctgry.0cuprofen 200mg tablet 0.110984
## elevmeds_ctgry.0cya - cyclosporin 0.407386
## elevmeds_ctgry.0desmopressin product 0.753420
## elevmeds_ctgry.0desmospray 10micrograms nasal spray 0.515565
## elevmeds_ctgry.0dexamethasone 0.404777
## elevmeds_ctgry.0diclofenac sodium+misoprostol 0.769149
## elevmeds_ctgry.0dicloflex 25mg e/c tablet 0.754464
## elevmeds_ctgry.0diclomax sr 75mg m/r capsule 0.306665
## elevmeds_ctgry.0dihydrocodeine 0.382455
## elevmeds_ctgry.0dipyridamole+aspirin 0.098366
## elevmeds_ctgry.0eccoxolac 300mg capsule 0.719810
## elevmeds_ctgry.0eltroxin 25micrograms tablet 0.411677
## elevmeds_ctgry.0entocort cr 3mg m/r capsule 0.581342
## elevmeds_ctgry.0ephedrine 0.706937
## elevmeds_ctgry.0epinephrine 0.285130
## elevmeds_ctgry.0epoetin beta 0.869194
## elevmeds_ctgry.0etodolac 0.852207
## elevmeds_ctgry.0etoricoxib 0.545396
## elevmeds_ctgry.0felbinac 0.701071
## elevmeds_ctgry.0fenactol 25mg e/c tablet 0.392974
## elevmeds_ctgry.0florinef 100mcg tablet 0.180887
## elevmeds_ctgry.0fludrocortisone 0.475623
## elevmeds_ctgry.0flurbiprofen 0.725576
## elevmeds_ctgry.0froben 50mg tablet 0.056142
## elevmeds_ctgry.0gabapentin 0.571641
## elevmeds_ctgry.0goserelin 0.489298
## elevmeds_ctgry.0growth hormone product 0.700253
## elevmeds_ctgry.0hydrocortisone 0.664173
## elevmeds_ctgry.0hydrocortisone product 0.446077
## elevmeds_ctgry.0hydrocortisone+clotrimazole 0.896731
## elevmeds_ctgry.0hydrocortisone+lidocaine 0.873468
## elevmeds_ctgry.0hydrocortisone+miconazole 0.745472
## elevmeds_ctgry.0ibuprofen 0.532055
## elevmeds_ctgry.0ibuprofen product 0.346058
## elevmeds_ctgry.0indomethacin 0.356851
## elevmeds_ctgry.0indomethacin product 0.476887
## elevmeds_ctgry.0ketoprofen 0.560754
## elevmeds_ctgry.0ketotifen 0.944460
## elevmeds_ctgry.0lamictal 25mg tablet 0.998753
## elevmeds_ctgry.0liothyronine 0.468480
## elevmeds_ctgry.0lithium product 0.842175
## elevmeds_ctgry.0lyrica 25mg capsule 0.745321
## elevmeds_ctgry.0meloxicam 0.709723
## elevmeds_ctgry.0mesren mr 400mg m/r tablet 0.527712
## elevmeds_ctgry.0methylprednisolone 0.437443
## elevmeds_ctgry.0micropirin 75mg e/c tablet 0.920261
## elevmeds_ctgry.0migril tablet 0.615360
## elevmeds_ctgry.0mobic 15mg tablet 0.893955
## elevmeds_ctgry.0mobic 7.5mg tablet 0.327330
## elevmeds_ctgry.0modafinil 0.778770
## elevmeds_ctgry.0nabumetone 0.450094
## elevmeds_ctgry.0napratec tablet combination pack 0.885239
## elevmeds_ctgry.0naprosyn 250mg tablet 0.881396
## elevmeds_ctgry.0naproxen 0.546541
## elevmeds_ctgry.0neurontin 100mg capsule 0.749683
## elevmeds_ctgry.0norditropin(epr) 12iu(4mg) injection (pdr for recon)+diluent 0.946464
## elevmeds_ctgry.0nuelin 125mg tablet 0.088778
## elevmeds_ctgry.0nurofen 200mg tablet 0.889507
## elevmeds_ctgry.0orphenadrine 0.168500
## elevmeds_ctgry.0oruvail 100 m/r capsule 0.235493
## elevmeds_ctgry.0oxcarbazepine 0.428913
## elevmeds_ctgry.0piroxicam 0.328822
## elevmeds_ctgry.0ponstan 250mg capsule 0.502986
## elevmeds_ctgry.0prednesol 5mg tablet 0.348459
## elevmeds_ctgry.0prednisolone 0.881948
## elevmeds_ctgry.0prednisolone product 0.897562
## elevmeds_ctgry.0prednisone 0.997965
## elevmeds_ctgry.0priadel 200mg m/r tablet 0.812553
## elevmeds_ctgry.0provigil 100mg tablet 0.240194
## elevmeds_ctgry.0pseudoephedrine 0.544272
## elevmeds_ctgry.0salicylic acid product 0.469042
## elevmeds_ctgry.0slo-phyllin 60mg m/r capsule 0.512035
## elevmeds_ctgry.0sodium thyroxine 0.345223
## elevmeds_ctgry.0sodium valproate 0.789860
## elevmeds_ctgry.0somatropin 0.026441
## elevmeds_ctgry.0surgam 200mg tablet 0.145576
## elevmeds_ctgry.0tacrolimus monohydrate 0.03% ointment 0.078379
## elevmeds_ctgry.0tenoxicam 0.790914
## elevmeds_ctgry.0theophylline product 0.974161
## elevmeds_ctgry.0thyroxine product 0.250064
## elevmeds_ctgry.0thyroxine sodium 0.216864
## elevmeds_ctgry.0triamcinolone 0.553333
## elevmeds_ctgry.0volsaid retard 75mg m/r tablet 0.362816
## elevmeds_ctgry.0voltaren retard 100mg m/r tablet 0.450491
## elevmeds_ctgry.0voltarene 25mg e/c tablet 0.256013
## mean_sbp.0 < 2e-16
## high_bp.0 < 2e-16
## htn_meds_count.0 0.481029
## f.21003.0.0 < 2e-16
## f.31.0.0 0.000209
## angina.0 < 2e-16
## heartattack.0 1.91e-10
## f.2443.0.0 < 2e-16
## depr_l.0 < 2e-16
## f.21001.0.0 < 2e-16
## mean_hr.0 < 2e-16
##
## (Intercept) ***
## elevmeds_ctgry.0acemetacin
## elevmeds_ctgry.0adrenaline product
## elevmeds_ctgry.0amphotericin
## elevmeds_ctgry.0anadin tablet
## elevmeds_ctgry.0arcoxia 120mg tablet
## elevmeds_ctgry.0arcoxia 60mg tablet
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## mean_sbp.0 ***
## high_bp.0 ***
## htn_meds_count.0
## f.21003.0.0 ***
## f.31.0.0 ***
## angina.0 ***
## heartattack.0 ***
## f.2443.0.0 ***
## depr_l.0 ***
## f.21001.0.0 ***
## mean_hr.0 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5728 on 41293 degrees of freedom
## (461085 observations deleted due to missingness)
## Multiple R-squared: 0.1063, Adjusted R-squared: 0.1038
## F-statistic: 42.7 on 115 and 41293 DF, p-value: < 2.2e-16# calculate delta adj. r squared
summary(mdl_fit888)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.09680388Diseases
partcipants with and without depression at initial assessment (f.20002 codes “depression” = 1286)
### participants with/without depression history only
# prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "f.21001.0.0", "mean_hr.0") # without depr_l.0
# depressive mood
outcome <- "depr_c.0"
dat_filtered <- filter(dat, depr_l.0 != 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, depr_c.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, f.21001.0.0, mean_hr.0))))
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "), paste(covs, collapse = " + "), sep = " + "), sep = " ~ "))
mdl_fit9 <- lm(mdl, data=dat_scaled)
summary(mdl_fit9)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9643 -0.6463 -0.3043 0.3846 5.6899
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.457e-14 1.836e-03 0.000 1.00000
## mean_sbp.0 -6.869e-02 2.069e-03 -33.195 < 2e-16 ***
## high_bp.0 4.558e-02 2.416e-03 18.862 < 2e-16 ***
## htn_meds_count.0 -7.964e-03 2.568e-03 -3.101 0.00193 **
## f.21003.0.0 -1.611e-01 1.993e-03 -80.816 < 2e-16 ***
## f.31.0.0 -6.091e-02 1.888e-03 -32.267 < 2e-16 ***
## angina.0 6.682e-02 2.031e-03 32.896 < 2e-16 ***
## heartattack.0 2.340e-02 2.012e-03 11.630 < 2e-16 ***
## f.2443.0.0 2.915e-02 1.919e-03 15.193 < 2e-16 ***
## f.21001.0.0 7.384e-02 1.962e-03 37.646 < 2e-16 ***
## mean_hr.0 5.184e-02 1.892e-03 27.401 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9737 on 281322 degrees of freedom
## Multiple R-squared: 0.05187, Adjusted R-squared: 0.05183
## F-statistic: 1539 on 10 and 281322 DF, p-value: < 2.2e-16car::vif(mdl_fit9)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.270557 1.732327 1.956795 1.178741
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.057092 1.224202 1.201171 1.092099
## f.21001.0.0 mean_hr.0
## 1.141652 1.062029dat_filtered <- filter(dat, depr_l.0 == 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, depr_c.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, f.21001.0.0, mean_hr.0))))
mdl_fit10 <- lm(mdl, data=dat_scaled)
summary(mdl_fit10)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1292 -0.7513 -0.1610 0.5064 3.2209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.941e-16 6.450e-03 0.000 1.000000
## mean_sbp.0 -5.201e-02 7.230e-03 -7.194 6.49e-13 ***
## high_bp.0 4.458e-02 8.385e-03 5.317 1.07e-07 ***
## htn_meds_count.0 5.655e-03 9.173e-03 0.616 0.537599
## f.21003.0.0 -1.820e-01 6.998e-03 -26.014 < 2e-16 ***
## f.31.0.0 4.043e-02 6.617e-03 6.110 1.01e-09 ***
## angina.0 5.597e-02 7.495e-03 7.467 8.49e-14 ***
## heartattack.0 2.092e-02 7.262e-03 2.881 0.003968 **
## f.2443.0.0 2.394e-02 6.810e-03 3.515 0.000441 ***
## f.21001.0.0 8.879e-02 6.942e-03 12.791 < 2e-16 ***
## mean_hr.0 9.190e-02 6.629e-03 13.863 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9662 on 22427 degrees of freedom
## Multiple R-squared: 0.06687, Adjusted R-squared: 0.06645
## F-statistic: 160.7 on 10 and 22427 DF, p-value: < 2.2e-16car::vif(mdl_fit10)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.256243 1.689753 2.022395 1.176938
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.052233 1.350150 1.267455 1.114657
## f.21001.0.0 mean_hr.0
## 1.158139 1.056163# well-being
outcome <- "wb.0"
dat_filtered <- filter(dat, depr_l.0 != 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, f.21001.0.0, mean_hr.0))))
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "), paste(covs, collapse = " + "), sep = " + "), sep = " ~ "))
mdl_fit11 <- lm(mdl, data=dat_scaled)
summary(mdl_fit11)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.6103 -0.6176 0.0189 0.6477 3.6671
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.232e-16 2.801e-03 0.000 1.000
## mean_sbp.0 5.767e-02 3.150e-03 18.306 < 2e-16 ***
## high_bp.0 -5.544e-02 3.702e-03 -14.979 < 2e-16 ***
## htn_meds_count.0 5.722e-03 3.921e-03 1.459 0.145
## f.21003.0.0 1.760e-01 3.041e-03 57.875 < 2e-16 ***
## f.31.0.0 -2.158e-02 2.871e-03 -7.516 5.68e-14 ***
## angina.0 -4.990e-02 3.071e-03 -16.246 < 2e-16 ***
## heartattack.0 -2.418e-02 3.044e-03 -7.944 1.97e-15 ***
## f.2443.0.0 -3.659e-02 2.922e-03 -12.521 < 2e-16 ***
## f.21001.0.0 -8.099e-02 2.992e-03 -27.064 < 2e-16 ***
## mean_hr.0 -4.689e-02 2.888e-03 -16.236 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9732 on 120725 degrees of freedom
## Multiple R-squared: 0.05295, Adjusted R-squared: 0.05287
## F-statistic: 674.9 on 10 and 120725 DF, p-value: < 2.2e-16car::vif(mdl_fit11)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.265033 1.746568 1.960026 1.178904
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.050388 1.202402 1.181322 1.088550
## f.21001.0.0 mean_hr.0
## 1.141535 1.063428dat_filtered <- filter(dat, depr_l.0 == 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, f.21001.0.0, mean_hr.0))))
mdl_fit12 <- lm(mdl, data=dat_scaled)
summary(mdl_fit12)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4687 -0.5033 0.0649 0.6018 3.5066
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.563e-16 9.904e-03 0.000 1.0000
## mean_sbp.0 5.877e-02 1.113e-02 5.280 1.32e-07 ***
## high_bp.0 -8.172e-02 1.312e-02 -6.229 4.89e-10 ***
## htn_meds_count.0 2.711e-02 1.400e-02 1.937 0.0528 .
## f.21003.0.0 2.370e-01 1.072e-02 22.100 < 2e-16 ***
## f.31.0.0 -9.622e-02 1.012e-02 -9.506 < 2e-16 ***
## angina.0 -4.845e-02 1.126e-02 -4.305 1.69e-05 ***
## heartattack.0 -2.503e-02 1.103e-02 -2.270 0.0232 *
## f.2443.0.0 -4.864e-02 1.040e-02 -4.676 2.97e-06 ***
## f.21001.0.0 -9.740e-02 1.073e-02 -9.076 < 2e-16 ***
## mean_hr.0 -8.183e-02 1.016e-02 -8.051 9.22e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9469 on 9129 degrees of freedom
## Multiple R-squared: 0.1044, Adjusted R-squared: 0.1034
## F-statistic: 106.4 on 10 and 9129 DF, p-value: < 2.2e-16car::vif(mdl_fit12)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.262983 1.754175 1.996952 1.172280
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.044376 1.291394 1.239900 1.103098
## f.21001.0.0 mean_hr.0
## 1.173793 1.052887## longitudinal
# prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.2, wb.2, phq9.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "f.21001.0.0", "mean_hr.0") # without depr_l.0
# depressive mood
outcome <- "phq9.0"
dat_filtered <- filter(dat, depr_l.0 != 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, phq9.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, f.21001.0.0, mean_hr.0))))
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "), paste(covs, collapse = " + "), sep = " + "), sep = " ~ "))
mdl_fit13 <- lm(mdl, data=dat_scaled)
summary(mdl_fit13)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8004 -0.6157 -0.2880 0.3032 7.1020
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.078e-14 3.154e-03 0.000 1.000000
## mean_sbp.0 -5.153e-02 3.604e-03 -14.297 < 2e-16 ***
## high_bp.0 3.496e-02 4.237e-03 8.251 < 2e-16 ***
## htn_meds_count.0 -7.025e-03 4.389e-03 -1.601 0.109483
## f.21003.0.0 -1.522e-01 3.406e-03 -44.685 < 2e-16 ***
## f.31.0.0 -7.933e-02 3.269e-03 -24.269 < 2e-16 ***
## angina.0 4.242e-02 3.413e-03 12.430 < 2e-16 ***
## heartattack.0 1.293e-02 3.401e-03 3.801 0.000144 ***
## f.2443.0.0 1.620e-02 3.258e-03 4.973 6.62e-07 ***
## f.21001.0.0 1.260e-01 3.373e-03 37.344 < 2e-16 ***
## mean_hr.0 2.941e-02 3.255e-03 9.037 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9729 on 95138 degrees of freedom
## Multiple R-squared: 0.05348, Adjusted R-squared: 0.05338
## F-statistic: 537.5 on 10 and 95138 DF, p-value: < 2.2e-16car::vif(mdl_fit13)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.305458 1.804429 1.936420 1.166284
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.073875 1.170724 1.162751 1.067042
## f.21001.0.0 mean_hr.0
## 1.143746 1.064839dat_filtered <- filter(dat, depr_l.0 == 1)
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat_filtered, phq9.0, mean_sbp.0, high_bp.0, htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, f.21001.0.0, mean_hr.0))))
mdl_fit14 <- lm(mdl, data=dat_scaled)
summary(mdl_fit14)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.9605 -0.6982 -0.2370 0.4720 3.8772
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.067e-16 1.155e-02 0.000 1.000000
## mean_sbp.0 -4.531e-02 1.306e-02 -3.471 0.000522 ***
## high_bp.0 3.274e-02 1.543e-02 2.121 0.033935 *
## htn_meds_count.0 1.275e-02 1.611e-02 0.791 0.428839
## f.21003.0.0 -1.656e-01 1.240e-02 -13.359 < 2e-16 ***
## f.31.0.0 -3.328e-03 1.185e-02 -0.281 0.778893
## angina.0 1.874e-02 1.272e-02 1.474 0.140498
## heartattack.0 1.524e-02 1.246e-02 1.223 0.221242
## f.2443.0.0 2.878e-02 1.207e-02 2.384 0.017130 *
## f.21001.0.0 1.761e-01 1.252e-02 14.066 < 2e-16 ***
## mean_hr.0 5.679e-02 1.192e-02 4.764 1.94e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9621 on 6930 degrees of freedom
## Multiple R-squared: 0.07577, Adjusted R-squared: 0.07443
## F-statistic: 56.81 on 10 and 6930 DF, p-value: < 2.2e-16car::vif(mdl_fit14)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.277998 1.786232 1.946834 1.152076
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.053194 1.212431 1.163855 1.091977
## f.21001.0.0 mean_hr.0
## 1.175158 1.065684partcipants without/with severe diseases only
# partcipants without/with severe diseases only
## NO DISEASES
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0, excl.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
d <- subset(dat, excl.0 != "excluded")
# define predictors and covariates
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(d, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0,
mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4964 -0.5965 -0.2817 0.3644 5.4122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.800e-15 1.902e-03 0.000 1
## f.21003.0.0 -1.681e-01 1.916e-03 -87.706 <2e-16 ***
## f.31.0.0 -5.522e-02 1.931e-03 -28.599 <2e-16 ***
## heartattack.0 1.843e-02 1.923e-03 9.583 <2e-16 ***
## f.2443.0.0 2.700e-02 1.944e-03 13.888 <2e-16 ***
## depr_l.0 2.573e-01 1.913e-03 134.506 <2e-16 ***
## f.21001.0.0 6.977e-02 1.958e-03 35.634 <2e-16 ***
## mean_hr.0 4.432e-02 1.947e-03 22.758 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9407 on 244637 degrees of freedom
## Multiple R-squared: 0.1152, Adjusted R-squared: 0.1151
## F-statistic: 4548 on 7 and 244637 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_d.0_excl0 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_d.0_excl0)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4016 -0.5963 -0.2785 0.3622 5.4122
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.387e-15 1.898e-03 0.000 1
## mean_sbp.0 -6.312e-02 2.165e-03 -29.157 <2e-16 ***
## high_bp.0 5.001e-02 2.580e-03 19.389 <2e-16 ***
## htn_meds_count.0 -2.228e-02 2.590e-03 -8.603 <2e-16 ***
## f.21003.0.0 -1.525e-01 2.061e-03 -74.018 <2e-16 ***
## f.31.0.0 -4.834e-02 1.952e-03 -24.764 <2e-16 ***
## heartattack.0 1.937e-02 1.976e-03 9.806 <2e-16 ***
## f.2443.0.0 2.447e-02 1.973e-03 12.403 <2e-16 ***
## depr_l.0 2.554e-01 1.912e-03 133.562 <2e-16 ***
## f.21001.0.0 7.353e-02 2.022e-03 36.364 <2e-16 ***
## mean_hr.0 4.625e-02 1.953e-03 23.679 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9388 on 244634 degrees of freedom
## Multiple R-squared: 0.1187, Adjusted R-squared: 0.1187
## F-statistic: 3295 on 10 and 244634 DF, p-value: < 2.2e-16car::vif(mdl_fit_d.0_excl0)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.300796 1.847056 1.861980 1.178606
## f.31.0.0 heartattack.0 f.2443.0.0 depr_l.0
## 1.057907 1.083536 1.080108 1.014649
## f.21001.0.0 mean_hr.0
## 1.135113 1.059156# calculate delta adj. r squared
summary(mdl_fit_d.0_excl0)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.00355593# well-being
outcome <- c("wb.0")
dat_scaled <- data.frame(scale(na.omit(dplyr::select(d, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.4837 -0.6094 0.0191 0.6365 4.0963
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.006e-17 2.971e-03 0.000 1
## f.21003.0.0 1.855e-01 2.991e-03 62.023 < 2e-16 ***
## f.31.0.0 -2.585e-02 3.016e-03 -8.571 < 2e-16 ***
## heartattack.0 -1.689e-02 3.001e-03 -5.628 1.83e-08 ***
## f.2443.0.0 -4.284e-02 3.036e-03 -14.112 < 2e-16 ***
## depr_l.0 -1.518e-01 2.987e-03 -50.807 < 2e-16 ***
## f.21001.0.0 -8.232e-02 3.057e-03 -26.928 < 2e-16 ***
## mean_hr.0 -4.350e-02 3.045e-03 -14.287 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9625 on 104922 degrees of freedom
## Multiple R-squared: 0.07374, Adjusted R-squared: 0.07368
## F-statistic: 1193 on 7 and 104922 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_wb.0_excl0 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_wb.0_excl0)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5501 -0.6049 0.0193 0.6346 4.0060
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -9.238e-16 2.965e-03 0.000 1
## mean_sbp.0 5.781e-02 3.372e-03 17.141 < 2e-16 ***
## high_bp.0 -6.389e-02 4.057e-03 -15.749 < 2e-16 ***
## htn_meds_count.0 2.144e-02 4.066e-03 5.271 1.36e-07 ***
## f.21003.0.0 1.737e-01 3.216e-03 54.011 < 2e-16 ***
## f.31.0.0 -2.936e-02 3.040e-03 -9.657 < 2e-16 ***
## heartattack.0 -1.771e-02 3.082e-03 -5.747 9.11e-09 ***
## f.2443.0.0 -3.880e-02 3.077e-03 -12.612 < 2e-16 ***
## depr_l.0 -1.504e-01 2.985e-03 -50.395 < 2e-16 ***
## f.21001.0.0 -8.245e-02 3.161e-03 -26.080 < 2e-16 ***
## mean_hr.0 -4.391e-02 3.052e-03 -14.384 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9604 on 104919 degrees of freedom
## Multiple R-squared: 0.07765, Adjusted R-squared: 0.07756
## F-statistic: 883.2 on 10 and 104919 DF, p-value: < 2.2e-16car::vif(mdl_fit_wb.0_excl0)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.293675 1.871889 1.881023 1.176461
## f.31.0.0 heartattack.0 f.2443.0.0 depr_l.0
## 1.051512 1.080652 1.076772 1.013415
## f.21001.0.0 mean_hr.0
## 1.136926 1.059894# calculate delta adj. r squared
summary(mdl_fit_wb.0_excl0)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.003878145## ONLY DISEASES
### prepare data
d <- subset(dat, excl.0 == "excluded")
### depressive mood
outcome <- "depr_c.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(d, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
heartattack.0, f.2443.0.0, depr_l.0, f.21001.0.0,
mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3988 -0.6182 -0.2511 0.4246 4.3344
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.350e-15 3.804e-03 0.000 1
## f.21003.0.0 -1.776e-01 3.890e-03 -45.659 <2e-16 ***
## f.31.0.0 -3.280e-02 3.920e-03 -8.368 <2e-16 ***
## heartattack.0 5.646e-02 3.948e-03 14.302 <2e-16 ***
## f.2443.0.0 4.974e-02 3.963e-03 12.552 <2e-16 ***
## depr_l.0 2.819e-01 3.838e-03 73.437 <2e-16 ***
## f.21001.0.0 7.475e-02 3.945e-03 18.945 <2e-16 ***
## mean_hr.0 6.610e-02 3.892e-03 16.983 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9251 on 59118 degrees of freedom
## Multiple R-squared: 0.1444, Adjusted R-squared: 0.1443
## F-statistic: 1425 on 7 and 59118 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "), paste(covs, collapse = " + "), sep = " + "), sep = " ~ "))
mdl_fit_d.0_excl1 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_d.0_excl1)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5187 -0.6141 -0.2519 0.4211 4.3827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.127e-15 3.794e-03 0.000 1
## mean_sbp.0 -5.514e-02 4.144e-03 -13.307 < 2e-16 ***
## high_bp.0 3.711e-02 4.658e-03 7.967 1.65e-15 ***
## htn_meds_count.0 2.788e-02 5.095e-03 5.471 4.48e-08 ***
## f.21003.0.0 -1.741e-01 4.140e-03 -42.048 < 2e-16 ***
## f.31.0.0 -3.284e-02 3.942e-03 -8.330 < 2e-16 ***
## heartattack.0 4.196e-02 4.145e-03 10.121 < 2e-16 ***
## f.2443.0.0 3.959e-02 4.028e-03 9.828 < 2e-16 ***
## depr_l.0 2.796e-01 3.832e-03 72.976 < 2e-16 ***
## f.21001.0.0 6.900e-02 4.089e-03 16.872 < 2e-16 ***
## mean_hr.0 7.041e-02 3.917e-03 17.974 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9226 on 59115 degrees of freedom
## Multiple R-squared: 0.1489, Adjusted R-squared: 0.1487
## F-statistic: 1034 on 10 and 59115 DF, p-value: < 2.2e-16car::vif(mdl_fit_d.0_excl1)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.192616 1.507274 1.802748 1.190316
## f.31.0.0 heartattack.0 f.2443.0.0 depr_l.0
## 1.079411 1.193594 1.127181 1.019832
## f.21001.0.0 mean_hr.0
## 1.161569 1.065743# calculate delta adj. r squared
summary(mdl_fit_d.0_excl1)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.004484996# well-being
outcome <- c("wb.0")
dat_scaled <- data.frame(scale(na.omit(dplyr::select(d, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, heartattack.0, f.2443.0.0,
depr_l.0, f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.6411 -0.5638 0.0265 0.6210 3.4142
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.223e-15 5.968e-03 0.000 1
## f.21003.0.0 2.043e-01 6.092e-03 33.527 < 2e-16 ***
## f.31.0.0 -3.291e-02 6.139e-03 -5.361 8.33e-08 ***
## heartattack.0 -6.357e-02 6.179e-03 -10.288 < 2e-16 ***
## f.2443.0.0 -4.714e-02 6.214e-03 -7.586 3.42e-14 ***
## depr_l.0 -2.021e-01 6.023e-03 -33.563 < 2e-16 ***
## f.21001.0.0 -8.469e-02 6.181e-03 -13.702 < 2e-16 ***
## mean_hr.0 -6.047e-02 6.100e-03 -9.913 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9427 on 24938 degrees of freedom
## Multiple R-squared: 0.1116, Adjusted R-squared: 0.1114
## F-statistic: 447.5 on 7 and 24938 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_wb.0_excl1 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_wb.0_excl1)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.5531 -0.5638 0.0262 0.6179 3.4081
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.449e-15 5.955e-03 0.000 1.0000
## mean_sbp.0 4.586e-02 6.498e-03 7.058 1.74e-12 ***
## high_bp.0 -4.524e-02 7.295e-03 -6.201 5.69e-10 ***
## htn_meds_count.0 -2.436e-02 7.989e-03 -3.049 0.0023 **
## f.21003.0.0 2.033e-01 6.509e-03 31.229 < 2e-16 ***
## f.31.0.0 -3.069e-02 6.173e-03 -4.971 6.70e-07 ***
## heartattack.0 -4.999e-02 6.469e-03 -7.727 1.14e-14 ***
## f.2443.0.0 -3.711e-02 6.318e-03 -5.874 4.30e-09 ***
## depr_l.0 -2.008e-01 6.013e-03 -33.391 < 2e-16 ***
## f.21001.0.0 -7.690e-02 6.422e-03 -11.976 < 2e-16 ***
## mean_hr.0 -6.372e-02 6.144e-03 -10.371 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9405 on 24935 degrees of freedom
## Multiple R-squared: 0.1158, Adjusted R-squared: 0.1154
## F-statistic: 326.5 on 10 and 24935 DF, p-value: < 2.2e-16car::vif(mdl_fit_wb.0_excl1)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.190562 1.500838 1.799650 1.194699
## f.31.0.0 heartattack.0 f.2443.0.0 depr_l.0
## 1.074517 1.180277 1.125705 1.019527
## f.21001.0.0 mean_hr.0
## 1.162886 1.064421# calculate delta adj. r squared
summary(mdl_fit_wb.0_excl1)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.004087021Exploratory analyses
Moderation analysis: Developing HTN
How does the relationship between SBP and depression differ between people who become hypertensive and those who don’t?
Inclusion: Participants who were not hypertensive at baseline. 1. HTN is defined as HTN diagnosis or intake of antiHTN meds at baseline. 2. Change ifelse statement below to also define SBP>140 mmHg as criterion for hypertension.
##
## No HTN HTN
## 315582 156052##
## No HTN HTN
## 26468 11563##
## No HTN
## 315582##
## No HTN HTN
## 22292 3699## `summarise()` ungrouping output (override with `.groups` argument)| hypertension.2 | n |
|---|---|
| 0 | 22292 |
| 1 | 3287 |
## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## Warning in table1.formula(~mean_sbp.0 + mean_sbp.2 + depr_c.0 + depr_c.2 + :
## Terms to the right of '|' in formula 'x' define table columns and are expected
## to be factors with meaningful labels.| No HTN (N=22292) |
HTN (N=3699) |
Overall (N=315582) |
|
|---|---|---|---|
| Systolic blood pressure (mmHg) | |||
| Mean (SD) | 130 (15.3) | 148 (18.7) | 134 (17.6) |
| Median [Min, Max] | 129 [84.0, 213] | 147 [94.0, 237] | 133 [65.0, 253] |
| Systolic blood pressure (mmHg) | |||
| Mean (SD) | 135 (17.9) | 143 (18.0) | 136 (18.1) |
| Median [Min, Max] | 134 [77.5, 231] | 143 [93.0, 224] | 135 [77.5, 231] |
| Missing | 0 (0%) | 412 (11.1%) | 290003 (91.9%) |
| Current depressive symptoms | |||
| Mean (SD) | 1.33 (0.446) | 1.33 (0.455) | 1.38 (0.508) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] |
| Missing | 1522 (6.8%) | 267 (7.2%) | 31814 (10.1%) |
| Current depressive symptoms (2nd follow-up) | |||
| Mean (SD) | 1.29 (0.431) | 1.31 (0.441) | 1.29 (0.433) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] |
| Missing | 1416 (6.4%) | 239 (6.5%) | 284341 (90.1%) |
| Well-being | |||
| Mean (SD) | 4.55 (0.532) | 4.54 (0.553) | 4.49 (0.568) |
| Median [Min, Max] | 4.50 [1.00, 6.00] | 4.50 [2.33, 6.00] | 4.50 [1.00, 6.00] |
| Missing | 14075 (63.1%) | 2472 (66.8%) | 198999 (63.1%) |
| Well-being (2nd follow-up) | |||
| Mean (SD) | 4.64 (0.539) | 4.60 (0.536) | 4.64 (0.538) |
| Median [Min, Max] | 4.67 [1.00, 6.00] | 4.60 [1.40, 6.00] | 4.67 [1.00, 6.00] |
| Missing | 122 (0.5%) | 28 (0.8%) | 282406 (89.5%) |
##
## Welch Two Sample t-test
##
## data: d$mean_sbp.0 by factor(d$hypertension.2)
## t = -53.315, df = 4553.6, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group No HTN and group HTN is not equal to 0
## 95 percent confidence interval:
## -17.90287 -16.63293
## sample estimates:
## mean in group No HTN mean in group HTN
## 130.3298 147.5977##
## Welch Two Sample t-test
##
## data: ukb_lbl$depr_c.0 by factor(ukb_lbl$hypertension.2)
## t = -3.6918, df = 19514, p-value = 0.0002233
## alternative hypothesis: true difference in means between group No HTN and group HTN is not equal to 0
## 95 percent confidence interval:
## -0.030638782 -0.009387438
## sample estimates:
## mean in group No HTN mean in group HTN
## 1.330789 1.350802##
## Welch Two Sample t-test
##
## data: d$depr_c.0 by factor(d$hypertension.2)
## t = -0.73832, df = 4585.9, p-value = 0.4604
## alternative hypothesis: true difference in means between group No HTN and group HTN is not equal to 0
## 95 percent confidence interval:
## -0.02256193 0.01021720
## sample estimates:
## mean in group No HTN mean in group HTN
## 1.327817 1.333989##
## Welch Two Sample t-test
##
## data: d$wb.0 by factor(d$hypertension.2)
## t = 0.66154, df = 1583.8, p-value = 0.5084
## alternative hypothesis: true difference in means between group No HTN and group HTN is not equal to 0
## 95 percent confidence interval:
## -0.02189931 0.04418850
## sample estimates:
## mean in group No HTN mean in group HTN
## 4.546651 4.535507##
## Call:
## lm(formula = depr_c.0 ~ hypertension.2, data = d_scaled)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -0.33399, -0.32782, -0.07782, 0.17218, 2.67218
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.327817 0.003102 428.097 <2e-16 ***
## hypertension.2HTN 0.006172 0.008237 0.749 0.454
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.447 on 24200 degrees of freedom
## (291380 observations deleted due to missingness)
## Multiple R-squared: 2.321e-05, Adjusted R-squared: -1.812e-05
## F-statistic: 0.5616 on 1 and 24200 DF, p-value: 0.4536##
## Call:
## lm(formula = depr_c.0 ~ mean_sbp.0 + hypertension.2, data = d_scaled)
##
## Residuals:
## LABEL: Current depressive symptoms
## VALUES:
## -0.50845, -0.31548, -0.1166, 0.16425, 2.77064
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.7680332 0.0237186 74.542 < 2e-16 ***
## mean_sbp.0 -0.0033772 0.0001804 -18.718 < 2e-16 ***
## hypertension.2HTN 0.0646316 0.0087538 7.383 1.59e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4438 on 24199 degrees of freedom
## (291380 observations deleted due to missingness)
## Multiple R-squared: 0.0143, Adjusted R-squared: 0.01421
## F-statistic: 175.5 on 2 and 24199 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = wb.0 ~ hypertension.2, data = d_scaled)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.5467, -0.3688, -0.0355, 0.2978, 1.4645
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.546651 0.005901 770.479 <2e-16 ***
## hypertension.2HTN -0.011145 0.016371 -0.681 0.496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5349 on 9442 degrees of freedom
## (306138 observations deleted due to missingness)
## Multiple R-squared: 4.908e-05, Adjusted R-squared: -5.683e-05
## F-statistic: 0.4634 on 1 and 9442 DF, p-value: 0.4961##
## Call:
## lm(formula = wb.0 ~ mean_sbp.0 + hypertension.2, data = d_scaled)
##
## Residuals:
## LABEL: Well-being
## VALUES:
## -3.5293, -0.3479, -9e-04, 0.3486, 1.5346
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.1742629 0.0456801 91.380 < 2e-16 ***
## mean_sbp.0 0.0028401 0.0003455 8.220 2.29e-16 ***
## hypertension.2HTN -0.0632575 0.0175024 -3.614 0.000303 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.533 on 9441 degrees of freedom
## (306138 observations deleted due to missingness)
## Multiple R-squared: 0.007156, Adjusted R-squared: 0.006945
## F-statistic: 34.02 on 2 and 9441 DF, p-value: 1.895e-15
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'##
## Call:
## lm(formula = depr_c.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4434 -0.6451 -0.3080 0.3460 6.4105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.005151 0.006626 0.777 0.4370
## mean_sbp.0 -0.081259 0.007379 -11.012 < 2e-16 ***
## hypertension.2 0.060468 0.007857 7.696 1.46e-14 ***
## f.21003.0.0 -0.143208 0.006645 -21.551 < 2e-16 ***
## f.31.0.0 -0.075182 0.006514 -11.542 < 2e-16 ***
## f.21001.0.0 0.087674 0.006488 13.513 < 2e-16 ***
## mean_sbp.0:hypertension.2 -0.014437 0.005905 -2.445 0.0145 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9769 on 24177 degrees of freedom
## Multiple R-squared: 0.04587, Adjusted R-squared: 0.04563
## F-statistic: 193.7 on 6 and 24177 DF, p-value: < 2.2e-16## mean_sbp.0 hypertension.2 f.21003.0.0
## 1.379655 1.564320 1.118865
## f.31.0.0 f.21001.0.0 mean_sbp.0:hypertension.2
## 1.075045 1.066709 1.505678##
## Call:
## lm(formula = depr_c.2 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.3831 -0.6349 -0.3216 0.2859 6.6839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.001707 0.006638 0.257 0.797
## mean_sbp.0 -0.060690 0.007394 -8.208 2.37e-16 ***
## hypertension.2 0.051682 0.007863 6.573 5.04e-11 ***
## f.21003.0.0 -0.135421 0.006646 -20.377 < 2e-16 ***
## f.31.0.0 -0.071522 0.006523 -10.964 < 2e-16 ***
## f.21001.0.0 0.079181 0.006502 12.177 < 2e-16 ***
## mean_sbp.0:hypertension.2 -0.004782 0.005912 -0.809 0.419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9813 on 24309 degrees of freedom
## Multiple R-squared: 0.03722, Adjusted R-squared: 0.03698
## F-statistic: 156.6 on 6 and 24309 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = wb.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.7755 -0.6211 0.0049 0.6399 3.1409
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006305 0.010649 -0.592 0.55382
## mean_sbp.0 0.060239 0.011745 5.129 2.98e-07 ***
## hypertension.2 -0.042817 0.013043 -3.283 0.00103 **
## f.21003.0.0 0.163504 0.010640 15.368 < 2e-16 ***
## f.31.0.0 -0.028568 0.010388 -2.750 0.00597 **
## f.21001.0.0 -0.102127 0.010403 -9.817 < 2e-16 ***
## mean_sbp.0:hypertension.2 0.017408 0.009600 1.813 0.06981 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9775 on 9425 degrees of freedom
## Multiple R-squared: 0.0451, Adjusted R-squared: 0.04449
## F-statistic: 74.19 on 6 and 9425 DF, p-value: < 2.2e-16## mean_sbp.0 hypertension.2 f.21003.0.0
## 1.361630 1.678995 1.117304
## f.31.0.0 f.21001.0.0 mean_sbp.0:hypertension.2
## 1.065006 1.068118 1.600867##
## Call:
## lm(formula = wb.2 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.5780 -0.6371 0.0250 0.6268 2.8604
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.0005987 0.0064422 -0.093 0.926
## mean_sbp.0 0.0515916 0.0071698 7.196 6.38e-13 ***
## hypertension.2 -0.0582252 0.0076318 -7.629 2.44e-14 ***
## f.21003.0.0 0.1580051 0.0064521 24.489 < 2e-16 ***
## f.31.0.0 -0.0059926 0.0063304 -0.947 0.344
## f.21001.0.0 -0.0768508 0.0063132 -12.173 < 2e-16 ***
## mean_sbp.0:hypertension.2 0.0016828 0.0057508 0.293 0.770
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9816 on 25812 degrees of freedom
## Multiple R-squared: 0.03678, Adjusted R-squared: 0.03656
## F-statistic: 164.3 on 6 and 25812 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = depr_c.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0 + angina.0 + heartattack.0 + f.2443.0.0 +
## depr_l.0 + mean_hr.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.1567 -0.6017 -0.2718 0.3526 5.7812
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.003542 0.008104 0.437 0.66208
## mean_sbp.0 -0.071490 0.009022 -7.924 2.46e-15 ***
## hypertension.2 0.053769 0.009484 5.670 1.46e-08 ***
## f.21003.0.0 -0.153340 0.008136 -18.847 < 2e-16 ***
## f.31.0.0 -0.055320 0.008047 -6.875 6.45e-12 ***
## f.21001.0.0 0.072458 0.008054 8.997 < 2e-16 ***
## angina.0 0.020284 0.007766 2.612 0.00901 **
## heartattack.0 0.004565 0.007764 0.588 0.55657
## f.2443.0.0 0.014041 0.007736 1.815 0.06955 .
## depr_l.0 0.260520 0.007715 33.768 < 2e-16 ***
## mean_hr.0 0.021471 0.007900 2.718 0.00658 **
## mean_sbp.0:hypertension.2 -0.009885 0.007303 -1.353 0.17594
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9371 on 14914 degrees of freedom
## Multiple R-squared: 0.1226, Adjusted R-squared: 0.1219
## F-statistic: 189.4 on 11 and 14914 DF, p-value: < 2.2e-16## mean_sbp.0 hypertension.2 f.21003.0.0
## 1.383461 1.528759 1.125134
## f.31.0.0 f.21001.0.0 angina.0
## 1.100535 1.102439 1.025064
## heartattack.0 f.2443.0.0 depr_l.0
## 1.024509 1.017333 1.011712
## mean_hr.0 mean_sbp.0:hypertension.2
## 1.060749 1.454837##
## Call:
## lm(formula = depr_c.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0 + f.2443.0.0 + mean_hr.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.4265 -0.6448 -0.3045 0.3474 6.4039
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.004900 0.006622 0.740 0.45936
## mean_sbp.0 -0.082849 0.007393 -11.207 < 2e-16 ***
## hypertension.2 0.057812 0.007872 7.344 2.13e-13 ***
## f.21003.0.0 -0.142980 0.006646 -21.514 < 2e-16 ***
## f.31.0.0 -0.070994 0.006595 -10.765 < 2e-16 ***
## f.21001.0.0 0.081798 0.006582 12.428 < 2e-16 ***
## f.2443.0.0 0.017331 0.006318 2.743 0.00609 **
## mean_hr.0 0.028594 0.006461 4.426 9.65e-06 ***
## mean_sbp.0:hypertension.2 -0.013735 0.005904 -2.326 0.02002 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9764 on 24175 degrees of freedom
## Multiple R-squared: 0.04698, Adjusted R-squared: 0.04666
## F-statistic: 149 on 8 and 24175 DF, p-value: < 2.2e-16## mean_sbp.0 hypertension.2 f.21003.0.0
## 1.386279 1.571779 1.120357
## f.31.0.0 f.21001.0.0 f.2443.0.0
## 1.103342 1.098819 1.012547
## mean_hr.0 mean_sbp.0:hypertension.2
## 1.058812 1.506697##
## Call:
## lm(formula = depr_c.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0 + angina.0 + heartattack.0 + f.2443.0.0 +
## depr_l.0 + mean_hr.0 + f.738.0.0 + education.0 + ethn.0 +
## f.1200.0.0 + f.54.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2240 -0.5792 -0.2193 0.3529 5.8053
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0022522 0.0081523 0.276 0.782345
## mean_sbp.0 -0.0674930 0.0090656 -7.445 1.03e-13 ***
## hypertension.2 0.0444104 0.0095995 4.626 3.76e-06 ***
## f.21003.0.0 -0.1813282 0.0084307 -21.508 < 2e-16 ***
## f.31.0.0 -0.0157477 0.0082627 -1.906 0.056685 .
## f.21001.0.0 0.0641021 0.0081124 7.902 2.95e-15 ***
## angina.0 0.0189282 0.0078302 2.417 0.015647 *
## heartattack.0 0.0009824 0.0078261 0.126 0.900109
## f.2443.0.0 0.0168437 0.0077849 2.164 0.030509 *
## depr_l.0 0.2399710 0.0077924 30.796 < 2e-16 ***
## mean_hr.0 0.0191593 0.0079528 2.409 0.016004 *
## f.738.0.0 -0.0615148 0.0080243 -7.666 1.89e-14 ***
## education.0 0.0004389 0.0077324 0.057 0.954733
## ethn.0 -0.0286776 0.0077771 -3.687 0.000227 ***
## f.1200.0.0 0.2134941 0.0078419 27.225 < 2e-16 ***
## f.54.0.0 -0.0044194 0.0077552 -0.570 0.568785
## mean_sbp.0:hypertension.2 -0.0062665 0.0073371 -0.854 0.393070
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9115 on 13946 degrees of freedom
## Multiple R-squared: 0.1701, Adjusted R-squared: 0.1691
## F-statistic: 178.6 on 16 and 13946 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = depr_c.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0 + f.2443.0.0 + mean_hr.0 + f.738.0.0 +
## education.0 + ethn.0 + f.1200.0.0 + f.54.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.8501 -0.6092 -0.2500 0.3513 6.3508
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0034600 0.0066333 0.522 0.60194
## mean_sbp.0 -0.0787271 0.0073982 -10.641 < 2e-16 ***
## hypertension.2 0.0489265 0.0079394 6.162 7.28e-10 ***
## f.21003.0.0 -0.1778025 0.0068475 -25.966 < 2e-16 ***
## f.31.0.0 -0.0267355 0.0067336 -3.970 7.20e-05 ***
## f.21001.0.0 0.0741233 0.0066021 11.227 < 2e-16 ***
## f.2443.0.0 0.0180158 0.0063267 2.848 0.00441 **
## mean_hr.0 0.0254695 0.0064754 3.933 8.40e-05 ***
## f.738.0.0 -0.0664948 0.0065254 -10.190 < 2e-16 ***
## education.0 -0.0004877 0.0063003 -0.077 0.93830
## ethn.0 -0.0291965 0.0063292 -4.613 3.99e-06 ***
## f.1200.0.0 0.2308627 0.0063823 36.173 < 2e-16 ***
## f.54.0.0 -0.0051295 0.0063099 -0.813 0.41626
## mean_sbp.0:hypertension.2 -0.0096405 0.0059023 -1.633 0.10241
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9468 on 22674 degrees of freedom
## Multiple R-squared: 0.104, Adjusted R-squared: 0.1035
## F-statistic: 202.5 on 13 and 22674 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = depr_c.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0 + angina.0 + f.2443.0.0 + depr_l.0 +
## mean_hr.0 + f.738.0.0 + ethn.0 + f.1200.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.2667 -0.5779 -0.2194 0.3511 5.7890
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.002466 0.007893 0.312 0.75474
## mean_sbp.0 -0.069426 0.008787 -7.901 2.95e-15 ***
## hypertension.2 0.046437 0.009236 5.028 5.02e-07 ***
## f.21003.0.0 -0.182932 0.008210 -22.282 < 2e-16 ***
## f.31.0.0 -0.015396 0.007989 -1.927 0.05398 .
## f.21001.0.0 0.066286 0.007846 8.449 < 2e-16 ***
## angina.0 0.017926 0.007486 2.395 0.01664 *
## f.2443.0.0 0.014689 0.007540 1.948 0.05143 .
## depr_l.0 0.241038 0.007544 31.949 < 2e-16 ***
## mean_hr.0 0.019870 0.007697 2.581 0.00985 **
## f.738.0.0 -0.062777 0.007805 -8.043 9.40e-16 ***
## ethn.0 -0.026992 0.007516 -3.591 0.00033 ***
## f.1200.0.0 0.211271 0.007601 27.794 < 2e-16 ***
## mean_sbp.0:hypertension.2 -0.006880 0.007113 -0.967 0.33342
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9113 on 14868 degrees of freedom
## Multiple R-squared: 0.1703, Adjusted R-squared: 0.1696
## F-statistic: 234.8 on 13 and 14868 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = wb.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0 + angina.0 + heartattack.0 + f.2443.0.0 +
## depr_l.0 + mean_hr.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0087 -0.6177 0.0117 0.6253 3.5827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.008128 0.013103 -0.620 0.535098
## mean_sbp.0 0.053829 0.014381 3.743 0.000183 ***
## hypertension.2 -0.035324 0.015859 -2.227 0.025956 *
## f.21003.0.0 0.171929 0.013118 13.107 < 2e-16 ***
## f.31.0.0 -0.045037 0.012909 -3.489 0.000489 ***
## f.21001.0.0 -0.086050 0.013018 -6.610 4.17e-11 ***
## angina.0 -0.005900 0.012504 -0.472 0.637061
## heartattack.0 0.013444 0.012500 1.075 0.282211
## f.2443.0.0 0.016036 0.012473 1.286 0.198602
## depr_l.0 -0.145702 0.012459 -11.695 < 2e-16 ***
## mean_hr.0 -0.033089 0.012740 -2.597 0.009420 **
## mean_sbp.0:hypertension.2 0.022697 0.011950 1.899 0.057559 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9642 on 6049 degrees of freedom
## Multiple R-squared: 0.07205, Adjusted R-squared: 0.07037
## F-statistic: 42.7 on 11 and 6049 DF, p-value: < 2.2e-16## mean_sbp.0 hypertension.2 f.21003.0.0
## 1.348127 1.639413 1.121665
## f.31.0.0 f.21001.0.0 angina.0
## 1.086275 1.104676 1.019144
## heartattack.0 f.2443.0.0 depr_l.0
## 1.018589 1.014122 1.011875
## mean_hr.0 mean_sbp.0:hypertension.2
## 1.058033 1.549808Brain imaging: Hariri task
Faces-shapes contrast in amygdala mask vs. whole-brain mask
## Warning: Removed 11334 rows containing non-finite values (stat_ydensity).## Warning: Removed 11334 rows containing non-finite values (stat_boxplot).## Warning: Removed 11334 rows containing non-finite values (stat_ydensity).## Warning: Removed 11334 rows containing non-finite values (stat_boxplot).
##
## Welch Two Sample t-test
##
## data: ukb_lbl$f.25054.2.0 by factor(ukb_lbl$hypertension.2)
## t = 9.7966, df = 15129, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group No HTN and group HTN is not equal to 0
## 95 percent confidence interval:
## 0.0913556 0.1370569
## sample estimates:
## mean in group No HTN mean in group HTN
## 1.248595 1.134389##
## Welch Two Sample t-test
##
## data: ukb_lbl$f.25050.2.0 by factor(ukb_lbl$hypertension.2)
## t = 14.528, df = 14888, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group No HTN and group HTN is not equal to 0
## 95 percent confidence interval:
## 0.2329510 0.3056172
## sample estimates:
## mean in group No HTN mean in group HTN
## 2.592095 2.322811## Warning: Removed 11334 rows containing non-finite values (stat_ydensity).
## Removed 11334 rows containing non-finite values (stat_boxplot).## Warning: Removed 13671 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 13671 rows containing non-finite values (stat_smooth).## Warning: Removed 13671 rows containing missing values (geom_point).## Warning: Removed 13671 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 13671 rows containing non-finite values (stat_smooth).## Warning: Removed 13671 rows containing missing values (geom_point).## Warning: Removed 11334 rows containing non-finite values (stat_ydensity).## Warning: Removed 11334 rows containing non-finite values (stat_boxplot).## Warning: Removed 472576 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 472576 rows containing non-finite values (stat_smooth).## Warning: Removed 472576 rows containing missing values (geom_point).## Warning: Removed 13671 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 13671 rows containing non-finite values (stat_smooth).## Warning: Removed 13671 rows containing missing values (geom_point).
## Warning: Removed 11334 rows containing non-finite values (stat_ydensity).## Warning: Removed 11334 rows containing non-finite values (stat_boxplot).## Warning: Removed 12243 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 12243 rows containing non-finite values (stat_smooth).## Warning: Removed 12243 rows containing missing values (geom_point).## Warning: Removed 12243 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 12243 rows containing non-finite values (stat_smooth).## Warning: Removed 12243 rows containing missing values (geom_point).## Warning: Removed 11334 rows containing non-finite values (stat_ydensity).## Warning: Removed 11334 rows containing non-finite values (stat_boxplot).## Warning: Removed 476706 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 476706 rows containing non-finite values (stat_smooth).## Warning: Removed 476706 rows containing missing values (geom_point).## Warning: Removed 12243 rows containing non-finite values (stat_density2d).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 12243 rows containing non-finite values (stat_smooth).## Warning: Removed 12243 rows containing missing values (geom_point).
##
## Call:
## lm(formula = f.25054.2.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2956 -0.6328 -0.0083 0.6306 3.9202
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.006790 0.006831 -0.994 0.32019
## mean_sbp.0 -0.036352 0.007362 -4.938 7.96e-07 ***
## hypertension.2 -0.033977 0.007416 -4.582 4.63e-06 ***
## f.21003.0.0 -0.134679 0.006759 -19.927 < 2e-16 ***
## f.31.0.0 0.070423 0.006532 10.782 < 2e-16 ***
## f.21001.0.0 0.012920 0.006633 1.948 0.05145 .
## mean_sbp.0:hypertension.2 0.017344 0.006604 2.626 0.00864 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9862 on 24326 degrees of freedom
## Multiple R-squared: 0.0276, Adjusted R-squared: 0.02736
## F-statistic: 115.1 on 6 and 24326 DF, p-value: < 2.2e-16## mean_sbp.0 hypertension.2 f.21003.0.0
## 1.355993 1.375702 1.142723
## f.31.0.0 f.21001.0.0 mean_sbp.0:hypertension.2
## 1.067230 1.100675 1.194138##
## Call:
## lm(formula = f.25050.2.0 ~ mean_sbp.0 * hypertension.2 + f.21003.0.0 +
## f.31.0.0 + f.21001.0.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.4243 -0.6338 0.0146 0.6491 3.5827
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003181 0.006747 -0.472 0.637
## mean_sbp.0 -0.032094 0.007272 -4.413 1.02e-05 ***
## hypertension.2 -0.030490 0.007325 -4.163 3.16e-05 ***
## f.21003.0.0 -0.192935 0.006676 -28.901 < 2e-16 ***
## f.31.0.0 0.062999 0.006451 9.765 < 2e-16 ***
## f.21001.0.0 -0.057097 0.006552 -8.715 < 2e-16 ***
## mean_sbp.0:hypertension.2 0.008126 0.006523 1.246 0.213
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9741 on 24326 degrees of freedom
## Multiple R-squared: 0.05131, Adjusted R-squared: 0.05108
## F-statistic: 219.3 on 6 and 24326 DF, p-value: < 2.2e-16##
## Call:
## lm(formula = f.25054.2.0 ~ mean_sbp.2 * hypertension.2 + f.21003.2.0 +
## f.31.0.0 + f.21001.2.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3495 -0.6354 -0.0030 0.6316 3.9713
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.002389 0.006297 -0.379 0.7044
## mean_sbp.2 -0.041289 0.006702 -6.160 7.36e-10 ***
## hypertension.2 -0.029559 0.006751 -4.379 1.20e-05 ***
## f.21003.2.0 -0.137841 0.006647 -20.737 < 2e-16 ***
## f.31.0.0 0.071340 0.006317 11.293 < 2e-16 ***
## f.21001.2.0 -0.001720 0.006480 -0.265 0.7907
## mean_sbp.2:hypertension.2 0.011851 0.006377 1.858 0.0631 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9854 on 25546 degrees of freedom
## Multiple R-squared: 0.02913, Adjusted R-squared: 0.0289
## F-statistic: 127.7 on 6 and 25546 DF, p-value: < 2.2e-16## mean_sbp.2 hypertension.2 f.21003.2.0
## 1.181984 1.199176 1.162612
## f.31.0.0 f.21001.2.0 mean_sbp.2:hypertension.2
## 1.049994 1.104810 1.064742##
## Call:
## lm(formula = f.25050.2.0 ~ mean_sbp.2 * hypertension.2 + htn_meds_count.2 +
## f.21003.2.0 + f.31.0.0 + f.21001.2.0, data = d_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3413 -0.6278 0.0127 0.6450 4.0766
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.003112 0.006201 -0.502 0.6158
## mean_sbp.2 -0.032164 0.006625 -4.855 1.21e-06 ***
## hypertension.2 -0.014321 0.011097 -1.291 0.1969
## htn_meds_count.2 -0.016086 0.010924 -1.473 0.1409
## f.21003.2.0 -0.210035 0.006582 -31.913 < 2e-16 ***
## f.31.0.0 0.061963 0.006226 9.953 < 2e-16 ***
## f.21001.2.0 -0.072139 0.006413 -11.250 < 2e-16 ***
## mean_sbp.2:hypertension.2 0.015438 0.006287 2.455 0.0141 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9703 on 25545 degrees of freedom
## Multiple R-squared: 0.05874, Adjusted R-squared: 0.05848
## F-statistic: 227.7 on 7 and 25545 DF, p-value: < 2.2e-16Comprehensive overview of results with confidence intervals and forest plots:
# forest plots
# main results from replication
cfs = c("Systolic blood pressure" = "mean_sbp.0",
"Diagnosed hypertension" = "high_bp.0",
"No. antihypertensive medication" = "htn_meds_count.0",
"Age" = "f.21003.0.0",
"Gender" = "f.31.0.0",
"Angina pectoris" = "angina.0",
"Heart attack" = "heartattack.0",
"Diabetes" = "f.2443.0.0",
"Lifetime depression" = "depr_l.0",
"BMI" = "f.21001.0.0",
"Heart rate" = "mean_hr.0")
# for models with additional covariates
cfs_covs = c("Systolic blood pressure" = "mean_sbp.0",
"Diagnosed hypertension" = "high_bp.0",
"No. antihypertensive medication" = "htn_meds_count.0",
"Age" = "f.21003.0.0",
"Gender" = "f.31.0.0",
"Angina pectoris" = "angina.0",
"Heart attack" = "heartattack.0",
"Diabetes" = "f.2443.0.0",
"Lifetime depression" = "depr_l.0",
"BMI" = "f.21001.0.0",
"Heart rate" = "mean_hr.0",
"Household income" = "f.738.0.0",
"Education" = "education.0",
"Ethnic background" = "ethn.0",
"Insomnia" = "f.1200.0.0",
"Assessment centre" = "f.54.0.0")
plot_summs(mdl_fit1, mdl_fit3,
coefs = cfs, model.names = c("Depressive symptoms (baseline)", "Well-being (baseline)"),
legend.title = "Outcomes",
colors = "Qual1") + labs(x="Standardized Beta") + xlim(c(-0.2, 0.3)) +
theme(text = element_text(size=16),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position = "top"
)## Loading required namespace: broom.mixed## Registered S3 methods overwritten by 'broom.mixed':
## method from
## augment.lme broom
## augment.merMod broom
## glance.lme broom
## glance.merMod broom
## glance.stanreg broom
## tidy.brmsfit broom
## tidy.gamlss broom
## tidy.lme broom
## tidy.merMod broom
## tidy.rjags broom
## tidy.stanfit broom
## tidy.stanreg broom
ggsave("forest_plot.0.png", width = 14, height = 5, device='png', dpi=600)
plot_summs(mdl_fit1_covs, mdl_fit3_covs,
coefs = cfs_covs, model.names = c("Depressive symptoms (baseline)", "Well-being (baseline)"),
legend.title = "Outcomes",
colors = "Qual1") + labs(x="Standardized Beta") + xlim(c(-0.2, 0.3)) +
theme(text = element_text(size=16),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position = "top"
)
ggsave("forest_plot_covs.0.png", width = 14, height = 5, device='png', dpi=600)
plot_summs(mdl_fit4, mdl_fit6, mdl_fit5,
coefs = cfs, model.names = c("Depressive symptoms (10y follow-up)",
"Well-being (10y follow-up)", "Depressive symptoms (6y follow-up)"),
legend.title = "Outcomes",
colors = "Qual1") + labs(x="Standardized Beta") + xlim(c(-0.2, 0.3)) +
theme(text = element_text(size=16),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position = "top")
ggsave("forest_plot.2s.png", width = 14, height = 5, device='png', dpi=600)
plot_summs(mdl_fit4, mdl_fit6,
coefs = cfs, model.names = c("Depressive symptoms (10y follow-up)",
"Well-being (10y follow-up)"),
legend.title = "Outcomes",
colors = "Qual1") + labs(x="Standardized Beta") + xlim(c(-0.2, 0.3)) +
theme(text = element_text(size=16),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position = "top") 
ggsave("forest_plot.2.png", width = 14, height = 5, device='png', dpi=600)
# effects of antiHTN meds
plot_summs(mdl_fit7, mdl_fit8,
model.names = c("Depression baseline", "Well-being baseline"),
colors = "Qual3")
# effects of antiDEPR meds
plot_summs(mdl_fit77, mdl_fit88,
model.names = c("Depression baseline", "Well-being baseline"),
colors = "Qual3")
# subgroup analysis of people without/with antiDEPR meds
plot_summs(mdl_fit_depr_meds0, mdl_fit_depr_meds1, coefs = cfs,
model.names = c("Depression baseline: no antidepressants",
"Depression baseline: antidepressants"))
# subgroup analysis of people without/with ANY meds
plot_summs(mdl_fit_meds0, mdl_fit_meds1,
model.names = c("Depression baseline: no meds", "Depression baseline: meds"))
# subgroup analysis of people without/with DEPRESSION
plot_summs(mdl_fit9, mdl_fit10,
model.names = c("Depression baseline: no depression", "Depression baseline: depression only"))
plot_summs(mdl_fit11, mdl_fit12,
model.names = c("Well-being baseline: no depression", "Well-being baseline: depression only"))
plot_summs(mdl_fit13, mdl_fit14,
model.names = c("PHQ9: no depression", "PHQ9: depression only"))
# subgroup analysis of people without/with DISEASES
plot_summs(mdl_fit_d.0_excl0, mdl_fit_wb.0_excl0, mdl_fit_d.0_excl1, mdl_fit_wb.0_excl1,
coefs = cfs,
model.names = c("Depression baseline: no diseases", "Well-being baseline: no diseases",
"Depression baseline: diseases", "Well-being baseline: diseases"),
colors = "Qual3")
summ(mdl_fit1, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 303771
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,303759) = 4094.282, p = 0.000
## R² = 0.129
## Adj. R² = 0.129
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.003 0.003 -0.000 1.000
## mean_sbp.0 -0.063 -0.067 -0.060 -33.170 0.000
## high_bp.0 0.043 0.039 0.047 19.341 0.000
## htn_meds_count.0 -0.006 -0.011 -0.001 -2.516 0.012
## f.21003.0.0 -0.156 -0.159 -0.152 -84.438 0.000
## f.31.0.0 -0.047 -0.051 -0.044 -27.028 0.000
## angina.0 0.063 0.059 0.067 33.456 0.000
## heartattack.0 0.022 0.018 0.025 11.757 0.000
## f.2443.0.0 0.027 0.024 0.030 15.250 0.000
## depr_l.0 0.264 0.261 0.267 154.831 0.000
## f.21001.0.0 0.072 0.069 0.076 39.905 0.000
## mean_hr.0 0.054 0.051 0.058 31.131 0.000
## -------------------------------------------------------------------summ(mdl_fit1_covs, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 247271
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(16,247254) = 3609.976, p = 0.000
## R² = 0.189
## Adj. R² = 0.189
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 17 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.004 0.004 0.000 1.000
## mean_sbp.0 -0.062 -0.066 -0.058 -30.001 0.000
## high_bp.0 0.034 0.029 0.039 14.129 0.000
## htn_meds_count.0 -0.011 -0.016 -0.006 -4.262 0.000
## f.21003.0.0 -0.192 -0.196 -0.188 -94.121 0.000
## f.31.0.0 -0.006 -0.009 -0.002 -3.021 0.003
## angina.0 0.043 0.039 0.047 21.608 0.000
## heartattack.0 0.013 0.009 0.017 6.484 0.000
## f.2443.0.0 0.015 0.011 0.019 7.923 0.000
## depr_l.0 0.245 0.241 0.248 133.637 0.000
## f.21001.0.0 0.060 0.056 0.064 31.039 0.000
## mean_hr.0 0.044 0.040 0.048 23.481 0.000
## f.738.0.0 -0.084 -0.088 -0.080 -44.256 0.000
## education.0 -0.007 -0.011 -0.004 -3.999 0.000
## ethn.0 -0.059 -0.062 -0.055 -31.725 0.000
## f.1200.0.0 0.224 0.221 0.228 121.800 0.000
## f.54.0.0 0.013 0.009 0.016 7.032 0.000
## -------------------------------------------------------------------summ(mdl_fit2, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 340900
## Dependent Variable: depr_l.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,340889) = 491.152, p = 0.000
## R² = 0.014
## Adj. R² = 0.014
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.003 0.003 0.000 1.000
## mean_sbp.0 -0.051 -0.055 -0.047 -26.672 0.000
## high_bp.0 -0.009 -0.013 -0.004 -3.921 0.000
## htn_meds_count.0 -0.006 -0.011 -0.002 -2.727 0.006
## f.21003.0.0 -0.057 -0.060 -0.053 -30.720 0.000
## f.31.0.0 -0.057 -0.060 -0.054 -32.592 0.000
## angina.0 0.015 0.011 0.019 7.898 0.000
## heartattack.0 -0.003 -0.007 0.000 -1.738 0.082
## f.2443.0.0 -0.005 -0.008 -0.001 -2.712 0.007
## f.21001.0.0 0.048 0.045 0.052 26.691 0.000
## mean_hr.0 0.018 0.014 0.021 10.198 0.000
## -------------------------------------------------------------------summ(mdl_fit3, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 129876
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,129864) = 1139.721, p = 0.000
## R² = 0.088
## Adj. R² = 0.088
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.005 0.005 0.000 1.000
## mean_sbp.0 0.057 0.051 0.063 18.990 0.000
## high_bp.0 -0.057 -0.064 -0.050 -16.254 0.000
## htn_meds_count.0 0.007 0.000 0.015 2.000 0.046
## f.21003.0.0 0.179 0.173 0.184 61.979 0.000
## f.31.0.0 -0.028 -0.033 -0.022 -10.179 0.000
## angina.0 -0.049 -0.055 -0.044 -16.967 0.000
## heartattack.0 -0.024 -0.029 -0.018 -8.248 0.000
## f.2443.0.0 -0.037 -0.042 -0.031 -13.331 0.000
## depr_l.0 -0.166 -0.171 -0.161 -62.149 0.000
## f.21001.0.0 -0.081 -0.087 -0.076 -28.679 0.000
## mean_hr.0 -0.050 -0.055 -0.045 -18.253 0.000
## -------------------------------------------------------------------summ(mdl_fit3_covs, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 106128
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(16,106111) = 1053.123, p = 0.000
## R² = 0.137
## Adj. R² = 0.137
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 17 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.006 0.006 0.000 1.000
## mean_sbp.0 0.055 0.049 0.061 16.954 0.000
## high_bp.0 -0.045 -0.052 -0.037 -11.732 0.000
## htn_meds_count.0 0.012 0.004 0.020 2.969 0.003
## f.21003.0.0 0.201 0.194 0.207 62.658 0.000
## f.31.0.0 -0.070 -0.076 -0.064 -23.356 0.000
## angina.0 -0.039 -0.045 -0.033 -12.589 0.000
## heartattack.0 -0.019 -0.025 -0.013 -6.112 0.000
## f.2443.0.0 -0.027 -0.033 -0.021 -9.129 0.000
## depr_l.0 -0.149 -0.154 -0.143 -51.561 0.000
## f.21001.0.0 -0.078 -0.084 -0.072 -25.514 0.000
## mean_hr.0 -0.037 -0.043 -0.032 -12.677 0.000
## f.738.0.0 0.117 0.111 0.123 39.121 0.000
## education.0 0.029 0.023 0.035 10.144 0.000
## ethn.0 0.066 0.060 0.072 22.168 0.000
## f.1200.0.0 -0.172 -0.177 -0.166 -59.140 0.000
## f.54.0.0 -0.041 -0.047 -0.036 -14.191 0.000
## -------------------------------------------------------------------summ(mdl_fit4, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 28021
## Dependent Variable: depr_c.2
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,28009) = 259.210, p = 0.000
## R² = 0.092
## Adj. R² = 0.092
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.011 0.011 -0.000 1.000
## mean_sbp.0 -0.042 -0.055 -0.029 -6.461 0.000
## high_bp.0 0.029 0.014 0.044 3.749 0.000
## htn_meds_count.0 -0.006 -0.022 0.009 -0.810 0.418
## f.21003.0.0 -0.136 -0.148 -0.124 -22.208 0.000
## f.31.0.0 -0.059 -0.071 -0.048 -10.027 0.000
## angina.0 0.033 0.021 0.045 5.319 0.000
## heartattack.0 0.018 0.006 0.030 3.011 0.003
## f.2443.0.0 0.018 0.007 0.029 3.080 0.002
## depr_l.0 0.224 0.213 0.235 39.084 0.000
## f.21001.0.0 0.074 0.062 0.086 12.148 0.000
## mean_hr.0 0.023 0.011 0.034 3.833 0.000
## -------------------------------------------------------------------summ(mdl_fit5, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 102090
## Dependent Variable: phq9.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,102078) = 1029.685, p = 0.000
## R² = 0.100
## Adj. R² = 0.100
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.006 0.006 0.000 1.000
## mean_sbp.0 -0.049 -0.056 -0.042 -14.440 0.000
## high_bp.0 0.033 0.026 0.041 8.367 0.000
## htn_meds_count.0 -0.005 -0.013 0.003 -1.211 0.226
## f.21003.0.0 -0.149 -0.155 -0.143 -46.351 0.000
## f.31.0.0 -0.069 -0.075 -0.063 -22.536 0.000
## angina.0 0.039 0.033 0.045 12.080 0.000
## heartattack.0 0.012 0.006 0.019 3.889 0.000
## f.2443.0.0 0.017 0.011 0.023 5.578 0.000
## depr_l.0 0.197 0.191 0.203 65.784 0.000
## f.21001.0.0 0.128 0.122 0.135 40.413 0.000
## mean_hr.0 0.032 0.026 0.038 10.347 0.000
## -------------------------------------------------------------------summ(mdl_fit6, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 29966
## Dependent Variable: wb.2
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,29954) = 160.125, p = 0.000
## R² = 0.056
## Adj. R² = 0.055
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.011 0.011 0.000 1.000
## mean_sbp.0 0.033 0.020 0.046 5.105 0.000
## high_bp.0 -0.032 -0.047 -0.017 -4.250 0.000
## htn_meds_count.0 -0.004 -0.019 0.011 -0.496 0.620
## f.21003.0.0 0.153 0.142 0.165 25.337 0.000
## f.31.0.0 -0.010 -0.021 0.002 -1.687 0.092
## angina.0 -0.032 -0.044 -0.020 -5.213 0.000
## heartattack.0 -0.009 -0.021 0.003 -1.511 0.131
## f.2443.0.0 -0.025 -0.036 -0.014 -4.344 0.000
## depr_l.0 -0.134 -0.145 -0.123 -23.730 0.000
## f.21001.0.0 -0.071 -0.083 -0.059 -11.836 0.000
## mean_hr.0 -0.025 -0.036 -0.013 -4.265 0.000
## -------------------------------------------------------------------summ(mdl_fit1_htn, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 107192
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,107181) = 1699.747, p = 0.000
## R² = 0.137
## Adj. R² = 0.137
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.006 0.006 -0.000 1.000
## mean_sbp.0 -0.054 -0.060 -0.048 -18.381 0.000
## htn_meds_count.0 -0.015 -0.022 -0.009 -4.773 0.000
## f.21003.0.0 -0.166 -0.172 -0.160 -54.939 0.000
## f.31.0.0 -0.048 -0.054 -0.042 -16.595 0.000
## angina.0 0.083 0.077 0.089 26.248 0.000
## heartattack.0 0.035 0.029 0.041 11.153 0.000
## f.2443.0.0 0.042 0.036 0.048 14.221 0.000
## depr_l.0 0.254 0.249 0.260 88.884 0.000
## f.21001.0.0 0.079 0.073 0.085 26.590 0.000
## mean_hr.0 0.044 0.039 0.050 15.263 0.000
## -------------------------------------------------------------------summ(mdl_fit3_htn, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 45319
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,45308) = 529.495, p = 0.000
## R² = 0.105
## Adj. R² = 0.104
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.009 0.009 -0.000 1.000
## mean_sbp.0 0.041 0.032 0.050 8.975 0.000
## htn_meds_count.0 0.010 0.000 0.020 2.023 0.043
## f.21003.0.0 0.202 0.192 0.211 42.734 0.000
## f.31.0.0 -0.007 -0.016 0.002 -1.613 0.107
## angina.0 -0.065 -0.075 -0.055 -13.203 0.000
## heartattack.0 -0.037 -0.046 -0.027 -7.603 0.000
## f.2443.0.0 -0.058 -0.067 -0.049 -12.487 0.000
## depr_l.0 -0.163 -0.172 -0.154 -36.432 0.000
## f.21001.0.0 -0.087 -0.096 -0.078 -18.653 0.000
## mean_hr.0 -0.041 -0.050 -0.032 -8.971 0.000
## -------------------------------------------------------------------summ(mdl_fit7, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 91000 (411494 missing obs. deleted)
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(34,90965) = 449.193, p = 0.000
## R² = 0.144
## Adj. R² = 0.143
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 35 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------------- -------- -------- -------- --------- -------
## (Intercept) 1.967 1.841 2.093 30.665 0.000
## htnmeds_ctgry.0ace -0.067 -0.180 0.047 -1.148 0.251
## inhibitor
## htnmeds_ctgry.0ace inhibitor 0.027 -0.186 0.240 0.250 0.803
## + diuretic
## htnmeds_ctgry.0ace inhibitor -0.110 -0.247 0.028 -1.560 0.119
## + thiazid
## htnmeds_ctgry.0aldosteron 0.069 -0.094 0.231 0.827 0.408
## antagonist
## htnmeds_ctgry.0alpha1 -0.024 -0.140 0.092 -0.404 0.686
## blocker
## htnmeds_ctgry.0alpha2 0.024 -0.098 0.146 0.384 0.701
## agonist
## htnmeds_ctgry.0at1 blocker -0.048 -0.162 0.066 -0.821 0.412
## htnmeds_ctgry.0at1 blocker + -0.047 -0.170 0.075 -0.758 0.448
## thiazid
## htnmeds_ctgry.0beta and -0.104 -0.288 0.081 -1.102 0.270
## alpha1 blocker
## htnmeds_ctgry.0beta -0.012 -0.126 0.102 -0.207 0.836
## blocker
## htnmeds_ctgry.0beta blocker -0.000 -0.129 0.128 -0.008 0.994
## + thiazid
## htnmeds_ctgry.0calcium -0.035 -0.148 0.079 -0.603 0.547
## antagonist
## htnmeds_ctgry.0calcium -0.216 -0.499 0.068 -1.492 0.136
## antagonist + ace inhibitor
## htnmeds_ctgry.0diuretic -0.039 -0.157 0.078 -0.654 0.513
## htnmeds_ctgry.0loop 0.100 -0.016 0.217 1.690 0.091
## diuretic
## htnmeds_ctgry.0loop diuretic -0.077 -0.273 0.118 -0.773 0.440
## + diuretic
## htnmeds_ctgry.0nitrate 0.082 -0.036 0.200 1.363 0.173
## htnmeds_ctgry.0no agonist -0.081 -0.320 0.157 -0.667 0.505
## htnmeds_ctgry.0phosphodiesterase3 -0.003 -0.286 0.281 -0.020 0.984
## inhibitor
## htnmeds_ctgry.0phosphodiesterase5 0.040 -0.078 0.159 0.666 0.505
## inhibitor
## htnmeds_ctgry.0synthetic -0.096 -0.408 0.216 -0.602 0.547
## amino acid
## htnmeds_ctgry.0thiazide -0.068 -0.182 0.045 -1.176 0.240
## htnmeds_ctgry.0thiazide + -0.048 -0.183 0.087 -0.698 0.485
## other diuretic
## mean_sbp.0 -0.001 -0.002 -0.001 -14.474 0.000
## high_bp.0 0.010 -0.001 0.020 1.812 0.070
## htn_meds_count.0 0.008 0.004 0.013 3.726 0.000
## f.21003.0.0 -0.014 -0.015 -0.014 -50.846 0.000
## f.31.0.0 -0.068 -0.076 -0.061 -19.093 0.000
## angina.0 0.146 0.134 0.158 24.216 0.000
## heartattack.0 0.044 0.031 0.057 6.537 0.000
## f.2443.0.0 0.063 0.053 0.072 12.892 0.000
## depr_l.0 0.587 0.573 0.601 82.577 0.000
## f.21001.0.0 0.008 0.007 0.009 23.351 0.000
## mean_hr.0 0.002 0.002 0.002 14.994 0.000
## ----------------------------------------------------------------------------------summ(mdl_fit8, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 39225 (463269 missing obs. deleted)
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(34,39190) = 130.848, p = 0.000
## R² = 0.102
## Adj. R² = 0.101
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 35 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------------- -------- -------- -------- --------- -------
## (Intercept) 3.869 3.701 4.037 45.148 0.000
## htnmeds_ctgry.0ace 0.049 -0.094 0.192 0.672 0.502
## inhibitor
## htnmeds_ctgry.0ace inhibitor -0.197 -0.533 0.139 -1.147 0.251
## + diuretic
## htnmeds_ctgry.0ace inhibitor 0.121 -0.079 0.321 1.188 0.235
## + thiazid
## htnmeds_ctgry.0aldosteron -0.261 -0.502 -0.020 -2.123 0.034
## antagonist
## htnmeds_ctgry.0alpha1 0.018 -0.131 0.166 0.237 0.813
## blocker
## htnmeds_ctgry.0alpha2 0.065 -0.096 0.225 0.790 0.429
## agonist
## htnmeds_ctgry.0at1 blocker 0.039 -0.104 0.183 0.538 0.591
## htnmeds_ctgry.0at1 blocker + 0.023 -0.139 0.185 0.282 0.778
## thiazid
## htnmeds_ctgry.0beta and 0.347 0.029 0.664 2.142 0.032
## alpha1 blocker
## htnmeds_ctgry.0beta 0.002 -0.141 0.144 0.024 0.981
## blocker
## htnmeds_ctgry.0beta blocker -0.043 -0.217 0.130 -0.492 0.623
## + thiazid
## htnmeds_ctgry.0calcium 0.012 -0.131 0.154 0.162 0.871
## antagonist
## htnmeds_ctgry.0calcium 0.184 -0.230 0.598 0.872 0.383
## antagonist + ace inhibitor
## htnmeds_ctgry.0diuretic 0.002 -0.148 0.152 0.025 0.980
## htnmeds_ctgry.0loop -0.067 -0.217 0.082 -0.884 0.377
## diuretic
## htnmeds_ctgry.0loop diuretic -0.122 -0.449 0.204 -0.735 0.463
## + diuretic
## htnmeds_ctgry.0nitrate -0.041 -0.194 0.113 -0.520 0.603
## htnmeds_ctgry.0no agonist -0.035 -0.410 0.341 -0.180 0.857
## htnmeds_ctgry.0phosphodiesterase3 0.245 -0.323 0.813 0.846 0.398
## inhibitor
## htnmeds_ctgry.0phosphodiesterase5 -0.021 -0.175 0.132 -0.274 0.784
## inhibitor
## htnmeds_ctgry.0synthetic 0.003 -0.468 0.474 0.011 0.991
## amino acid
## htnmeds_ctgry.0thiazide 0.058 -0.085 0.201 0.795 0.427
## htnmeds_ctgry.0thiazide + 0.129 -0.059 0.317 1.345 0.179
## other diuretic
## mean_sbp.0 0.001 0.001 0.002 8.856 0.000
## high_bp.0 -0.028 -0.045 -0.011 -3.222 0.001
## htn_meds_count.0 -0.010 -0.018 -0.003 -2.786 0.005
## f.21003.0.0 0.018 0.017 0.019 38.542 0.000
## f.31.0.0 0.008 -0.004 0.020 1.334 0.182
## angina.0 -0.125 -0.145 -0.105 -12.286 0.000
## heartattack.0 -0.067 -0.090 -0.045 -5.933 0.000
## f.2443.0.0 -0.095 -0.110 -0.080 -12.303 0.000
## depr_l.0 -0.395 -0.419 -0.371 -32.861 0.000
## f.21001.0.0 -0.008 -0.009 -0.007 -14.402 0.000
## mean_hr.0 -0.002 -0.002 -0.002 -8.151 0.000
## ----------------------------------------------------------------------------------summ(mdl_fit77, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 28722 (473772 missing obs. deleted)
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(16,28705) = 227.744, p = 0.000
## R² = 0.113
## Adj. R² = 0.112
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 17 > 1' in
## coercion to 'logical(1)'## -----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## -------------------------------- -------- -------- -------- --------- -------
## (Intercept) 2.420 2.123 2.716 16.006 0.000
## deprmeds_ctgry.0selective -0.250 -0.513 0.013 -1.863 0.062
## serotonin reuptake inhibitor
## deprmeds_ctgry.0selective -0.176 -0.441 0.090 -1.298 0.194
## serotonin-noradrelin reuptake
## inhibitor
## deprmeds_ctgry.0selective -0.134 -0.403 0.135 -0.977 0.329
## serotonin-noradrenalin
## reuptake inhibitor
## deprmeds_ctgry.0tricyclic -0.296 -0.558 -0.033 -2.205 0.027
## antidepressant
## deprmeds_ctgry.0unselective -0.354 -0.623 -0.084 -2.572 0.010
## reuptake inhibitor
## mean_sbp.0 -0.002 -0.002 -0.001 -6.540 0.000
## high_bp.0 0.071 0.048 0.095 6.004 0.000
## htn_meds_count.0 -0.005 -0.017 0.007 -0.874 0.382
## f.21003.0.0 -0.019 -0.021 -0.018 -31.788 0.000
## f.31.0.0 0.079 0.060 0.098 8.231 0.000
## angina.0 0.229 0.185 0.272 10.297 0.000
## heartattack.0 0.121 0.065 0.176 4.292 0.000
## f.2443.0.0 0.051 0.020 0.083 3.212 0.001
## depr_l.0 0.276 0.257 0.295 28.816 0.000
## f.21001.0.0 0.012 0.011 0.014 14.737 0.000
## mean_hr.0 0.004 0.003 0.005 10.561 0.000
## -----------------------------------------------------------------------------summ(mdl_fit88, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 12046 (490448 missing obs. deleted)
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(16,12029) = 104.843, p = 0.000
## R² = 0.122
## Adj. R² = 0.121
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 17 > 1' in
## coercion to 'logical(1)'## -----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## -------------------------------- -------- -------- -------- --------- -------
## (Intercept) 3.827 3.412 4.242 18.072 0.000
## deprmeds_ctgry.0selective 0.058 -0.312 0.429 0.309 0.758
## serotonin reuptake inhibitor
## deprmeds_ctgry.0selective -0.058 -0.432 0.316 -0.303 0.762
## serotonin-noradrelin reuptake
## inhibitor
## deprmeds_ctgry.0selective -0.060 -0.441 0.321 -0.310 0.757
## serotonin-noradrenalin
## reuptake inhibitor
## deprmeds_ctgry.0tricyclic 0.010 -0.360 0.381 0.055 0.956
## antidepressant
## deprmeds_ctgry.0unselective 0.001 -0.385 0.387 0.006 0.995
## reuptake inhibitor
## mean_sbp.0 0.002 0.001 0.002 4.322 0.000
## high_bp.0 -0.100 -0.133 -0.068 -6.073 0.000
## htn_meds_count.0 0.015 -0.002 0.031 1.743 0.081
## f.21003.0.0 0.021 0.020 0.023 25.608 0.000
## f.31.0.0 -0.148 -0.174 -0.122 -11.183 0.000
## angina.0 -0.150 -0.211 -0.090 -4.859 0.000
## heartattack.0 -0.120 -0.197 -0.043 -3.067 0.002
## f.2443.0.0 -0.091 -0.133 -0.050 -4.295 0.000
## depr_l.0 -0.208 -0.234 -0.182 -15.698 0.000
## f.21001.0.0 -0.012 -0.014 -0.010 -10.323 0.000
## mean_hr.0 -0.003 -0.004 -0.002 -5.061 0.000
## -----------------------------------------------------------------------------summ(mdl_fit_depr_meds0, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 275049
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,275037) = 1745.119, p = 0.000
## R² = 0.065
## Adj. R² = 0.065
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.004 0.004 -0.000 1.000
## mean_sbp.0 -0.064 -0.068 -0.060 -30.800 0.000
## high_bp.0 0.046 0.042 0.051 19.130 0.000
## htn_meds_count.0 -0.013 -0.018 -0.008 -5.063 0.000
## f.21003.0.0 -0.160 -0.164 -0.156 -79.728 0.000
## f.31.0.0 -0.053 -0.057 -0.049 -27.890 0.000
## angina.0 0.061 0.057 0.065 30.137 0.000
## heartattack.0 0.022 0.018 0.026 10.735 0.000
## f.2443.0.0 0.027 0.023 0.031 14.159 0.000
## depr_l.0 0.128 0.125 0.132 69.395 0.000
## f.21001.0.0 0.066 0.062 0.070 33.395 0.000
## mean_hr.0 0.043 0.040 0.047 22.798 0.000
## -------------------------------------------------------------------summ(mdl_fit_depr_meds1, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 28722
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,28710) = 324.362, p = 0.000
## R² = 0.111
## Adj. R² = 0.110
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.011 0.011 -0.000 1.000
## mean_sbp.0 -0.042 -0.055 -0.030 -6.845 0.000
## high_bp.0 0.045 0.030 0.059 6.109 0.000
## htn_meds_count.0 -0.009 -0.025 0.007 -1.138 0.255
## f.21003.0.0 -0.197 -0.209 -0.185 -32.289 0.000
## f.31.0.0 0.049 0.037 0.060 8.480 0.000
## angina.0 0.067 0.054 0.079 10.267 0.000
## heartattack.0 0.028 0.015 0.040 4.404 0.000
## f.2443.0.0 0.020 0.008 0.031 3.336 0.001
## depr_l.0 0.191 0.179 0.202 33.425 0.000
## f.21001.0.0 0.091 0.079 0.103 15.161 0.000
## mean_hr.0 0.060 0.048 0.071 10.345 0.000
## -------------------------------------------------------------------summ(mdl_fit_wb_depr_meds0, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 117830
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,117818) = 671.624, p = 0.000
## R² = 0.059
## Adj. R² = 0.059
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.006 0.006 -0.000 1.000
## mean_sbp.0 0.056 0.050 0.062 17.632 0.000
## high_bp.0 -0.057 -0.064 -0.050 -15.274 0.000
## htn_meds_count.0 0.009 0.002 0.017 2.365 0.018
## f.21003.0.0 0.175 0.169 0.181 57.016 0.000
## f.31.0.0 -0.025 -0.031 -0.020 -8.712 0.000
## angina.0 -0.048 -0.054 -0.042 -15.671 0.000
## heartattack.0 -0.023 -0.029 -0.017 -7.583 0.000
## f.2443.0.0 -0.037 -0.043 -0.031 -12.512 0.000
## depr_l.0 -0.084 -0.089 -0.078 -29.508 0.000
## f.21001.0.0 -0.077 -0.083 -0.071 -25.588 0.000
## mean_hr.0 -0.043 -0.049 -0.037 -14.702 0.000
## -------------------------------------------------------------------summ(mdl_fit_wb_depr_meds1, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 12046
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,12034) = 149.676, p = 0.000
## R² = 0.120
## Adj. R² = 0.120
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.017 0.017 0.000 1.000
## mean_sbp.0 0.040 0.021 0.058 4.161 0.000
## high_bp.0 -0.068 -0.090 -0.046 -6.015 0.000
## htn_meds_count.0 0.019 -0.005 0.043 1.574 0.116
## f.21003.0.0 0.238 0.219 0.256 25.443 0.000
## f.31.0.0 -0.098 -0.116 -0.081 -11.193 0.000
## angina.0 -0.047 -0.067 -0.028 -4.821 0.000
## heartattack.0 -0.029 -0.047 -0.010 -3.033 0.002
## f.2443.0.0 -0.038 -0.056 -0.021 -4.246 0.000
## depr_l.0 -0.140 -0.157 -0.123 -16.058 0.000
## f.21001.0.0 -0.095 -0.113 -0.077 -10.233 0.000
## mean_hr.0 -0.058 -0.076 -0.041 -6.618 0.000
## -------------------------------------------------------------------summ(mdl_fit_meds0, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 51698
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,51687) = 348.534, p = 0.000
## R² = 0.063
## Adj. R² = 0.063
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.008 0.008 -0.000 1.000
## mean_sbp.0 -0.080 -0.089 -0.070 -15.980 0.000
## high_bp.0 0.068 0.059 0.077 14.759 0.000
## f.21003.0.0 -0.147 -0.156 -0.138 -32.497 0.000
## f.31.0.0 -0.027 -0.036 -0.019 -6.225 0.000
## angina.0 0.044 0.035 0.052 10.211 0.000
## heartattack.0 0.009 0.000 0.017 2.034 0.042
## f.2443.0.0 0.001 -0.008 0.009 0.155 0.877
## depr_l.0 0.147 0.138 0.155 34.357 0.000
## f.21001.0.0 0.030 0.021 0.039 6.753 0.000
## mean_hr.0 0.035 0.026 0.043 7.931 0.000
## ----------------------------------------------------------------summ(mdl_fit_meds1, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 252073
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,252062) = 4019.882, p = 0.000
## R² = 0.138
## Adj. R² = 0.138
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.004 0.004 0.000 1.000
## mean_sbp.0 -0.057 -0.061 -0.053 -27.614 0.000
## high_bp.0 0.027 0.023 0.031 13.350 0.000
## f.21003.0.0 -0.167 -0.171 -0.163 -83.967 0.000
## f.31.0.0 -0.043 -0.047 -0.040 -22.697 0.000
## angina.0 0.062 0.058 0.066 31.327 0.000
## heartattack.0 0.020 0.016 0.024 10.152 0.000
## f.2443.0.0 0.024 0.020 0.028 12.459 0.000
## depr_l.0 0.271 0.268 0.275 145.399 0.000
## f.21001.0.0 0.073 0.069 0.077 37.176 0.000
## mean_hr.0 0.055 0.051 0.059 28.900 0.000
## ----------------------------------------------------------------summ(mdl_fit_wb_meds0, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24076
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,24065) = 143.819, p = 0.000
## R² = 0.056
## Adj. R² = 0.056
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.012 0.012 0.000 1.000
## mean_sbp.0 0.067 0.052 0.081 9.165 0.000
## high_bp.0 -0.076 -0.089 -0.063 -11.281 0.000
## f.21003.0.0 0.154 0.141 0.167 23.138 0.000
## f.31.0.0 -0.056 -0.068 -0.043 -8.677 0.000
## angina.0 -0.032 -0.044 -0.019 -5.038 0.000
## heartattack.0 -0.008 -0.021 0.004 -1.306 0.192
## f.2443.0.0 -0.011 -0.023 0.002 -1.697 0.090
## depr_l.0 -0.084 -0.096 -0.071 -13.357 0.000
## f.21001.0.0 -0.064 -0.077 -0.051 -9.714 0.000
## mean_hr.0 -0.053 -0.065 -0.040 -8.185 0.000
## ----------------------------------------------------------------summ(mdl_fit_wb_meds1, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 105800
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,105789) = 1098.492, p = 0.000
## R² = 0.094
## Adj. R² = 0.094
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.006 0.006 -0.000 1.000
## mean_sbp.0 0.053 0.046 0.059 16.145 0.000
## high_bp.0 -0.041 -0.048 -0.035 -12.943 0.000
## f.21003.0.0 0.190 0.184 0.196 60.407 0.000
## f.31.0.0 -0.028 -0.034 -0.022 -9.192 0.000
## angina.0 -0.050 -0.056 -0.044 -15.935 0.000
## heartattack.0 -0.023 -0.029 -0.017 -7.337 0.000
## f.2443.0.0 -0.037 -0.043 -0.031 -12.169 0.000
## depr_l.0 -0.172 -0.178 -0.166 -58.365 0.000
## f.21001.0.0 -0.079 -0.085 -0.073 -25.556 0.000
## mean_hr.0 -0.047 -0.053 -0.041 -15.649 0.000
## ----------------------------------------------------------------summ(mdl_fit9, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 281333
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,281322) = 1538.906, p = 0.000
## R² = 0.052
## Adj. R² = 0.052
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.004 0.004 -0.000 1.000
## mean_sbp.0 -0.069 -0.073 -0.065 -33.195 0.000
## high_bp.0 0.046 0.041 0.050 18.862 0.000
## htn_meds_count.0 -0.008 -0.013 -0.003 -3.101 0.002
## f.21003.0.0 -0.161 -0.165 -0.157 -80.816 0.000
## f.31.0.0 -0.061 -0.065 -0.057 -32.267 0.000
## angina.0 0.067 0.063 0.071 32.896 0.000
## heartattack.0 0.023 0.019 0.027 11.630 0.000
## f.2443.0.0 0.029 0.025 0.033 15.193 0.000
## f.21001.0.0 0.074 0.070 0.078 37.646 0.000
## mean_hr.0 0.052 0.048 0.056 27.401 0.000
## -------------------------------------------------------------------summ(mdl_fit10, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 22438
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,22427) = 160.708, p = 0.000
## R² = 0.067
## Adj. R² = 0.066
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.013 0.013 -0.000 1.000
## mean_sbp.0 -0.052 -0.066 -0.038 -7.194 0.000
## high_bp.0 0.045 0.028 0.061 5.317 0.000
## htn_meds_count.0 0.006 -0.012 0.024 0.616 0.538
## f.21003.0.0 -0.182 -0.196 -0.168 -26.014 0.000
## f.31.0.0 0.040 0.027 0.053 6.110 0.000
## angina.0 0.056 0.041 0.071 7.467 0.000
## heartattack.0 0.021 0.007 0.035 2.881 0.004
## f.2443.0.0 0.024 0.011 0.037 3.515 0.000
## f.21001.0.0 0.089 0.075 0.102 12.791 0.000
## mean_hr.0 0.092 0.079 0.105 13.863 0.000
## -------------------------------------------------------------------summ(mdl_fit11, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 120736
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,120725) = 674.920, p = 0.000
## R² = 0.053
## Adj. R² = 0.053
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.005 0.005 -0.000 1.000
## mean_sbp.0 0.058 0.051 0.064 18.306 0.000
## high_bp.0 -0.055 -0.063 -0.048 -14.979 0.000
## htn_meds_count.0 0.006 -0.002 0.013 1.459 0.145
## f.21003.0.0 0.176 0.170 0.182 57.875 0.000
## f.31.0.0 -0.022 -0.027 -0.016 -7.516 0.000
## angina.0 -0.050 -0.056 -0.044 -16.246 0.000
## heartattack.0 -0.024 -0.030 -0.018 -7.944 0.000
## f.2443.0.0 -0.037 -0.042 -0.031 -12.521 0.000
## f.21001.0.0 -0.081 -0.087 -0.075 -27.064 0.000
## mean_hr.0 -0.047 -0.053 -0.041 -16.236 0.000
## -------------------------------------------------------------------summ(mdl_fit12, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 9140
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,9129) = 106.392, p = 0.000
## R² = 0.104
## Adj. R² = 0.103
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## ------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- -------- -------
## (Intercept) 0.000 -0.019 0.019 0.000 1.000
## mean_sbp.0 0.059 0.037 0.081 5.280 0.000
## high_bp.0 -0.082 -0.107 -0.056 -6.229 0.000
## htn_meds_count.0 0.027 -0.000 0.055 1.937 0.053
## f.21003.0.0 0.237 0.216 0.258 22.100 0.000
## f.31.0.0 -0.096 -0.116 -0.076 -9.506 0.000
## angina.0 -0.048 -0.071 -0.026 -4.305 0.000
## heartattack.0 -0.025 -0.047 -0.003 -2.270 0.023
## f.2443.0.0 -0.049 -0.069 -0.028 -4.676 0.000
## f.21001.0.0 -0.097 -0.118 -0.076 -9.076 0.000
## mean_hr.0 -0.082 -0.102 -0.062 -8.051 0.000
## ------------------------------------------------------------------summ(mdl_fit13, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 95149
## Dependent Variable: phq9.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,95138) = 537.543, p = 0.000
## R² = 0.053
## Adj. R² = 0.053
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.006 0.006 -0.000 1.000
## mean_sbp.0 -0.052 -0.059 -0.044 -14.297 0.000
## high_bp.0 0.035 0.027 0.043 8.251 0.000
## htn_meds_count.0 -0.007 -0.016 0.002 -1.601 0.109
## f.21003.0.0 -0.152 -0.159 -0.146 -44.685 0.000
## f.31.0.0 -0.079 -0.086 -0.073 -24.269 0.000
## angina.0 0.042 0.036 0.049 12.430 0.000
## heartattack.0 0.013 0.006 0.020 3.801 0.000
## f.2443.0.0 0.016 0.010 0.023 4.973 0.000
## f.21001.0.0 0.126 0.119 0.133 37.344 0.000
## mean_hr.0 0.029 0.023 0.036 9.037 0.000
## -------------------------------------------------------------------summ(mdl_fit14, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 6941
## Dependent Variable: phq9.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,6930) = 56.812, p = 0.000
## R² = 0.076
## Adj. R² = 0.074
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.023 0.023 -0.000 1.000
## mean_sbp.0 -0.045 -0.071 -0.020 -3.471 0.001
## high_bp.0 0.033 0.002 0.063 2.121 0.034
## htn_meds_count.0 0.013 -0.019 0.044 0.791 0.429
## f.21003.0.0 -0.166 -0.190 -0.141 -13.359 0.000
## f.31.0.0 -0.003 -0.027 0.020 -0.281 0.779
## angina.0 0.019 -0.006 0.044 1.474 0.140
## heartattack.0 0.015 -0.009 0.040 1.223 0.221
## f.2443.0.0 0.029 0.005 0.052 2.384 0.017
## f.21001.0.0 0.176 0.152 0.201 14.066 0.000
## mean_hr.0 0.057 0.033 0.080 4.764 0.000
## -------------------------------------------------------------------summ(mdl_fit_d.0_excl0, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 244645
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,244634) = 3295.438, p = 0.000
## R² = 0.119
## Adj. R² = 0.119
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.004 0.004 -0.000 1.000
## mean_sbp.0 -0.063 -0.067 -0.059 -29.157 0.000
## high_bp.0 0.050 0.045 0.055 19.389 0.000
## htn_meds_count.0 -0.022 -0.027 -0.017 -8.603 0.000
## f.21003.0.0 -0.153 -0.157 -0.148 -74.018 0.000
## f.31.0.0 -0.048 -0.052 -0.045 -24.764 0.000
## heartattack.0 0.019 0.016 0.023 9.806 0.000
## f.2443.0.0 0.024 0.021 0.028 12.403 0.000
## depr_l.0 0.255 0.252 0.259 133.562 0.000
## f.21001.0.0 0.074 0.070 0.077 36.364 0.000
## mean_hr.0 0.046 0.042 0.050 23.679 0.000
## -------------------------------------------------------------------summ(mdl_fit_wb.0_excl0, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 104930
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,104919) = 883.223, p = 0.000
## R² = 0.078
## Adj. R² = 0.078
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.006 0.006 -0.000 1.000
## mean_sbp.0 0.058 0.051 0.064 17.141 0.000
## high_bp.0 -0.064 -0.072 -0.056 -15.749 0.000
## htn_meds_count.0 0.021 0.013 0.029 5.271 0.000
## f.21003.0.0 0.174 0.167 0.180 54.011 0.000
## f.31.0.0 -0.029 -0.035 -0.023 -9.657 0.000
## heartattack.0 -0.018 -0.024 -0.012 -5.747 0.000
## f.2443.0.0 -0.039 -0.045 -0.033 -12.612 0.000
## depr_l.0 -0.150 -0.156 -0.145 -50.395 0.000
## f.21001.0.0 -0.082 -0.089 -0.076 -26.080 0.000
## mean_hr.0 -0.044 -0.050 -0.038 -14.384 0.000
## -------------------------------------------------------------------summ(mdl_fit_d.0_excl1, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 59126
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,59115) = 1034.164, p = 0.000
## R² = 0.149
## Adj. R² = 0.149
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.007 0.007 -0.000 1.000
## mean_sbp.0 -0.055 -0.063 -0.047 -13.307 0.000
## high_bp.0 0.037 0.028 0.046 7.967 0.000
## htn_meds_count.0 0.028 0.018 0.038 5.471 0.000
## f.21003.0.0 -0.174 -0.182 -0.166 -42.048 0.000
## f.31.0.0 -0.033 -0.041 -0.025 -8.330 0.000
## heartattack.0 0.042 0.034 0.050 10.121 0.000
## f.2443.0.0 0.040 0.032 0.047 9.828 0.000
## depr_l.0 0.280 0.272 0.287 72.976 0.000
## f.21001.0.0 0.069 0.061 0.077 16.872 0.000
## mean_hr.0 0.070 0.063 0.078 17.974 0.000
## -------------------------------------------------------------------summ(mdl_fit_wb.0_excl1, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24946
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,24935) = 326.542, p = 0.000
## R² = 0.116
## Adj. R² = 0.115
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.012 0.012 0.000 1.000
## mean_sbp.0 0.046 0.033 0.059 7.058 0.000
## high_bp.0 -0.045 -0.060 -0.031 -6.201 0.000
## htn_meds_count.0 -0.024 -0.040 -0.009 -3.049 0.002
## f.21003.0.0 0.203 0.191 0.216 31.229 0.000
## f.31.0.0 -0.031 -0.043 -0.019 -4.971 0.000
## heartattack.0 -0.050 -0.063 -0.037 -7.727 0.000
## f.2443.0.0 -0.037 -0.049 -0.025 -5.874 0.000
## depr_l.0 -0.201 -0.213 -0.189 -33.391 0.000
## f.21001.0.0 -0.077 -0.089 -0.064 -11.976 0.000
## mean_hr.0 -0.064 -0.076 -0.052 -10.371 0.000
## -------------------------------------------------------------------summ(mdl_fit_d.0_mock, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24184
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(6,24177) = 193.698, p = 0.000
## R² = 0.046
## Adj. R² = 0.046
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 7 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- --------- -------
## (Intercept) 0.005 -0.008 0.018 0.777 0.437
## mean_sbp.0 -0.081 -0.096 -0.067 -11.012 0.000
## hypertension.2 0.060 0.045 0.076 7.696 0.000
## f.21003.0.0 -0.143 -0.156 -0.130 -21.551 0.000
## f.31.0.0 -0.075 -0.088 -0.062 -11.542 0.000
## f.21001.0.0 0.088 0.075 0.100 13.513 0.000
## mean_sbp.0:hypertension.2 -0.014 -0.026 -0.003 -2.445 0.015
## ----------------------------------------------------------------------------summ(mdl_fit_d.2_mock, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24316
## Dependent Variable: depr_c.2
## Type: OLS linear regression
##
## MODEL FIT:
## F(6,24309) = 156.610, p = 0.000
## R² = 0.037
## Adj. R² = 0.037
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 7 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- --------- -------
## (Intercept) 0.002 -0.011 0.015 0.257 0.797
## mean_sbp.0 -0.061 -0.075 -0.046 -8.208 0.000
## hypertension.2 0.052 0.036 0.067 6.573 0.000
## f.21003.0.0 -0.135 -0.148 -0.122 -20.377 0.000
## f.31.0.0 -0.072 -0.084 -0.059 -10.964 0.000
## f.21001.0.0 0.079 0.066 0.092 12.177 0.000
## mean_sbp.0:hypertension.2 -0.005 -0.016 0.007 -0.809 0.419
## ----------------------------------------------------------------------------summ(mdl_fit_wb.0_mock, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 9432
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(6,9425) = 74.191, p = 0.000
## R² = 0.045
## Adj. R² = 0.044
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 7 > 1' in
## coercion to 'logical(1)'## ---------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- -------- -------
## (Intercept) -0.006 -0.027 0.015 -0.592 0.554
## mean_sbp.0 0.060 0.037 0.083 5.129 0.000
## hypertension.2 -0.043 -0.068 -0.017 -3.283 0.001
## f.21003.0.0 0.164 0.143 0.184 15.368 0.000
## f.31.0.0 -0.029 -0.049 -0.008 -2.750 0.006
## f.21001.0.0 -0.102 -0.123 -0.082 -9.817 0.000
## mean_sbp.0:hypertension.2 0.017 -0.001 0.036 1.813 0.070
## ---------------------------------------------------------------------------summ(mdl_fit_wb.2_mock, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 25819
## Dependent Variable: wb.2
## Type: OLS linear regression
##
## MODEL FIT:
## F(6,25812) = 164.282, p = 0.000
## R² = 0.037
## Adj. R² = 0.037
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 7 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- --------- -------
## (Intercept) -0.001 -0.013 0.012 -0.093 0.926
## mean_sbp.0 0.052 0.038 0.066 7.196 0.000
## hypertension.2 -0.058 -0.073 -0.043 -7.629 0.000
## f.21003.0.0 0.158 0.145 0.171 24.489 0.000
## f.31.0.0 -0.006 -0.018 0.006 -0.947 0.344
## f.21001.0.0 -0.077 -0.089 -0.064 -12.173 0.000
## mean_sbp.0:hypertension.2 0.002 -0.010 0.013 0.293 0.770
## ----------------------------------------------------------------------------summ(mdl_fit_d.0_mock_BPuncorr, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24202 (291380 missing obs. deleted)
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(1,24200) = 0.562, p = 0.454
## R² = 0.000
## Adj. R² = -0.000
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 2 > 1' in
## coercion to 'logical(1)'## ------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ----------------------- ------- -------- ------- --------- -------
## (Intercept) 1.328 1.322 1.334 428.097 0.000
## hypertension.2HTN 0.006 -0.010 0.022 0.749 0.454
## ------------------------------------------------------------------summ(mdl_fit_d.0_mock_BPcorr, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24202 (291380 missing obs. deleted)
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(2,24199) = 175.475, p = 0.000
## R² = 0.014
## Adj. R² = 0.014
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 3 > 1' in
## coercion to 'logical(1)'## --------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ----------------------- -------- -------- -------- --------- -------
## (Intercept) 1.768 1.722 1.815 74.542 0.000
## mean_sbp.0 -0.003 -0.004 -0.003 -18.718 0.000
## hypertension.2HTN 0.065 0.047 0.082 7.383 0.000
## --------------------------------------------------------------------summ(mdl_fit_wb.0_mock_BPuncorr, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 9444 (306138 missing obs. deleted)
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(1,9442) = 0.463, p = 0.496
## R² = 0.000
## Adj. R² = -0.000
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 2 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ----------------------- -------- -------- ------- --------- -------
## (Intercept) 4.547 4.535 4.558 770.479 0.000
## hypertension.2HTN -0.011 -0.043 0.021 -0.681 0.496
## -------------------------------------------------------------------summ(mdl_fit_wb.0_mock_BPcorr, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 9444 (306138 missing obs. deleted)
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(2,9441) = 34.021, p = 0.000
## R² = 0.007
## Adj. R² = 0.007
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 3 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ----------------------- -------- -------- -------- -------- -------
## (Intercept) 4.174 4.085 4.264 91.380 0.000
## mean_sbp.0 0.003 0.002 0.004 8.220 0.000
## hypertension.2HTN -0.063 -0.098 -0.029 -3.614 0.000
## -------------------------------------------------------------------summ(mdl_fit_hariri_amy, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24333
## Dependent Variable: f.25054.2.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(6,24326) = 115.085, p = 0.000
## R² = 0.028
## Adj. R² = 0.027
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 7 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- --------- -------
## (Intercept) -0.007 -0.020 0.007 -0.994 0.320
## mean_sbp.0 -0.036 -0.051 -0.022 -4.938 0.000
## hypertension.2 -0.034 -0.049 -0.019 -4.582 0.000
## f.21003.0.0 -0.135 -0.148 -0.121 -19.927 0.000
## f.31.0.0 0.070 0.058 0.083 10.782 0.000
## f.21001.0.0 0.013 -0.000 0.026 1.948 0.051
## mean_sbp.0:hypertension.2 0.017 0.004 0.030 2.626 0.009
## ----------------------------------------------------------------------------summ(mdl_fit_hariri_brain, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 24333
## Dependent Variable: f.25050.2.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(6,24326) = 219.275, p = 0.000
## R² = 0.051
## Adj. R² = 0.051
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 7 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- --------- -------
## (Intercept) -0.003 -0.016 0.010 -0.472 0.637
## mean_sbp.0 -0.032 -0.046 -0.018 -4.413 0.000
## hypertension.2 -0.030 -0.045 -0.016 -4.163 0.000
## f.21003.0.0 -0.193 -0.206 -0.180 -28.901 0.000
## f.31.0.0 0.063 0.050 0.076 9.765 0.000
## f.21001.0.0 -0.057 -0.070 -0.044 -8.715 0.000
## mean_sbp.0:hypertension.2 0.008 -0.005 0.021 1.246 0.213
## ----------------------------------------------------------------------------summ(mdl_fit_hariri_amy.2, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 25553
## Dependent Variable: f.25054.2.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(6,25546) = 127.741, p = 0.000
## R² = 0.029
## Adj. R² = 0.029
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 7 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- --------- -------
## (Intercept) -0.002 -0.015 0.010 -0.379 0.704
## mean_sbp.2 -0.041 -0.054 -0.028 -6.160 0.000
## hypertension.2 -0.030 -0.043 -0.016 -4.379 0.000
## f.21003.2.0 -0.138 -0.151 -0.125 -20.737 0.000
## f.31.0.0 0.071 0.059 0.084 11.293 0.000
## f.21001.2.0 -0.002 -0.014 0.011 -0.265 0.791
## mean_sbp.2:hypertension.2 0.012 -0.001 0.024 1.858 0.063
## ----------------------------------------------------------------------------summ(mdl_fit_hariri_brain.2, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 25553
## Dependent Variable: f.25050.2.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(7,25545) = 227.740, p = 0.000
## R² = 0.059
## Adj. R² = 0.058
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 8 > 1' in
## coercion to 'logical(1)'## ----------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ------------------------------- -------- -------- -------- --------- -------
## (Intercept) -0.003 -0.015 0.009 -0.502 0.616
## mean_sbp.2 -0.032 -0.045 -0.019 -4.855 0.000
## hypertension.2 -0.014 -0.036 0.007 -1.291 0.197
## htn_meds_count.2 -0.016 -0.037 0.005 -1.473 0.141
## f.21003.2.0 -0.210 -0.223 -0.197 -31.913 0.000
## f.31.0.0 0.062 0.050 0.074 9.953 0.000
## f.21001.2.0 -0.072 -0.085 -0.060 -11.250 0.000
## mean_sbp.2:hypertension.2 0.015 0.003 0.028 2.455 0.014
## ----------------------------------------------------------------------------Hospital records (HES) analyses
1. Descriptives and agreement with self-report
get coding from ukbiobank website: https://biobank.ndph.ox.ac.uk/showcase/coding.cgi?id=19
#install.packages("irr")
library(irr)## Loading required package: lpSolve### Hospital records (primary "f.41202." and secondary "f.41204." ICD10 diagnoses)
coding19 <- read.csv("schaare/bp_mood_prereg/data/coding19.tsv", sep = "\t")
# count frequencies of different diagnosis categories
hes.0 <- ukb %>%
select(starts_with("f.41204.0.")) %>%
gather(.) %>%
group_by(as.factor(.$value)) %>%
tally(.)
# match to the name of diagnosis
hes.0 <- hes.0 %>%
merge(coding19, ., by.x = "coding", by.y = names(.)[1]) %>%
arrange(n)
# save results
#write.table(meds.0, file = "/data/ukb_hes.csv", sep = "\t", row.names = F, qmethod = "double")
# plot the diagnostic categories with names (only the top 50)
# lollipop plot
hes.0 %>%
filter(!is.na(meaning)) %>%
mutate(meaning=factor(meaning, levels=meaning)) %>%
tail(50) %>%
ggplot( aes(x=meaning, y=n)) +
geom_segment( aes(xend=meaning, yend=0)) +
geom_point( size=1, color="orange") +
xlab("ICD10 secondary diagnoses (top 50)") +
coord_flip() +
theme_bw()
# any HES diagnosis for each participant
hes_subj.0 <- ukb %>%
select(f.eid, starts_with("f.41204.0.")) %>%
mutate_at(., dplyr::vars(-f.eid), ~(!is.na(.)))
# find presence of any HES diagnosis per participant
hes_subj.0 <- hes_subj.0 %>%
mutate(hes.0 = rowSums(.[,-1]))
hes_subj.0 <- mutate(hes_subj.0, hes.0 = ifelse(hes.0 > 0, 1, NA))
# add count to ukb data
ukb <- as_tibble(merge(ukb, hes_subj.0[-c(2:185)], by = "f.eid"))
ukb_lbl <- as_tibble(merge(ukb_lbl, hes_subj.0[-c(2:185)], by = "f.eid"))
# How many people have hospital records?
table(ukb$hes.0) # N = 340896, 161598 NAs##
## 1
## 340896### HTN: Agreement self-report and HES
# HTN diagnosis for each participant
hes_htn_subj.0 <- ukb %>%
select(f.eid, starts_with("f.41204.0.")) %>%
mutate_at(., dplyr::vars(-f.eid), ~(match(., "I10")))
# find HTN diagnosis per participant
hes_htn_subj.0 <- hes_htn_subj.0 %>%
mutate(hes_htn.0 = rowSums(!is.na(.[,-1])))
# add to ukb data
ukb <- as_tibble(merge(ukb, hes_htn_subj.0[-c(2:185)], by = "f.eid"))
ukb_lbl <- as_tibble(merge(ukb_lbl, hes_htn_subj.0[-c(2:185)], by = "f.eid"))
# compute inter-rater reliability
d <- subset(ukb_lbl, !is.na(hes.0))
table(d$hes_htn.0)##
## 0 1
## 228342 112554table(d$hes_htn.0, d$high_bp.0)##
## No diagnosed HTN or unknown Diagnosed HTN
## 0 199628 28296
## 1 33741 78551ct <- dplyr::select(d, hes_htn.0, high_bp.0)
ct <- drop_na(ct) # N = 340216
kappa2(ct) # Kappa = 0.583 -> moderate agreement## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 340216
## Raters = 2
## Kappa = 0.583
##
## z = 340
## p-value = 0### Depression: Agreement self-report and HES
# Depression diagnosis for each participant
hes_cols <- ukb %>%
select(starts_with("f.41204.0.")) %>% names()
# remove depression with psychosis
hes_depr_subj.0 <- ukb %>%
select(f.eid, starts_with("f.41204.0.")) %>%
mutate_at(dplyr::vars(hes_cols), ~na_if(., "F323"))## Note: Using an external vector in selections is ambiguous.
## i Use `all_of(hes_cols)` instead of `hes_cols` to silence this message.
## i See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.x <- data.frame(rowSums(hes_depr_subj.0 == "F320", na.rm = T))
x[,2] <- rowSums(hes_depr_subj.0 == "F321", na.rm = T)
x[,3] <- rowSums(hes_depr_subj.0 == "F328", na.rm = T)
x[,4] <- rowSums(hes_depr_subj.0 == "F329", na.rm = T)
x[,5] <- rowSums(hes_depr_subj.0 == "F330", na.rm = T)
x[,6] <- rowSums(hes_depr_subj.0 == "F331", na.rm = T)
x[,7] <- rowSums(hes_depr_subj.0 == "F332", na.rm = T)
x[,8] <- rowSums(hes_depr_subj.0 == "F334", na.rm = T)
x[,9] <- rowSums(hes_depr_subj.0 == "F338", na.rm = T)
x[,10] <- rowSums(hes_depr_subj.0 == "F339", na.rm = T)
x[,11] <- rowSums(x, na.rm=T)
hes_depr_subj.0 <- cbind(hes_depr_subj.0, hes_depr.0 = x[,11])
# find Depr diagnosis per participant
hes_depr_subj.0 <- mutate(hes_depr_subj.0, hes_depr.0 = ifelse(hes_depr.0 > 0, 1, 0))
# add to ukb data
ukb <- as_tibble(merge(ukb, hes_depr_subj.0[-c(2:185)], by = "f.eid"))
ukb_lbl <- as_tibble(merge(ukb_lbl, hes_depr_subj.0[-c(2:185)], by = "f.eid"))
# compute inter-rater reliability
d <- subset(ukb_lbl, !is.na(hes.0))
table(d$hes_depr.0)##
## 0 1
## 322622 18274table(d$hes_depr.0, d$depr_l.0)##
## No diagnosed depression or unknown Diagnosed depression
## 0 244101 14983
## 1 9642 7265ct <- dplyr::select(d, hes_depr.0, depr_l.0)
ct <- drop_na(ct) # N = 275991
kappa2(ct) # Kappa = 0.324 -> fair agreement## Cohen's Kappa for 2 Raters (Weights: unweighted)
##
## Subjects = 275991
## Raters = 2
## Kappa = 0.324
##
## z = 172
## p-value = 02. Depression outcome from HES
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, hes_depr.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0,
f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
### HES depression
outcome <- "hes_depr.0"
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "f.21001.0.0", "mean_hr.0")
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, hes_depr.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8689 -0.2646 -0.2035 -0.1453 4.8256
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.842e-15 1.705e-03 0.000 1
## f.21003.0.0 -3.266e-02 1.725e-03 -18.935 < 2e-16 ***
## f.31.0.0 -4.351e-02 1.733e-03 -25.110 < 2e-16 ***
## angina.0 2.920e-02 1.831e-03 15.948 < 2e-16 ***
## heartattack.0 1.430e-02 1.827e-03 7.827 5.01e-15 ***
## f.2443.0.0 1.115e-02 1.753e-03 6.360 2.01e-10 ***
## f.21001.0.0 5.360e-02 1.759e-03 30.474 < 2e-16 ***
## mean_hr.0 3.222e-02 1.747e-03 18.444 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9958 on 340892 degrees of freedom
## Multiple R-squared: 0.008445, Adjusted R-squared: 0.008425
## F-statistic: 414.8 on 7 and 340892 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit2_hes <- lm(mdl, data=dat_scaled)
summary(mdl_fit2_hes)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.9085 -0.2702 -0.2041 -0.1391 4.8902
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.017e-15 1.704e-03 0.000 1.000000
## mean_sbp.0 -3.956e-02 1.920e-03 -20.608 < 2e-16 ***
## high_bp.0 9.481e-03 2.238e-03 4.237 2.27e-05 ***
## htn_meds_count.0 8.250e-03 2.383e-03 3.461 0.000538 ***
## f.21003.0.0 -2.358e-02 1.852e-03 -12.733 < 2e-16 ***
## f.31.0.0 -3.917e-02 1.751e-03 -22.373 < 2e-16 ***
## angina.0 2.531e-02 1.894e-03 13.361 < 2e-16 ***
## heartattack.0 1.059e-02 1.871e-03 5.660 1.52e-08 ***
## f.2443.0.0 7.846e-03 1.779e-03 4.411 1.03e-05 ***
## f.21001.0.0 5.599e-02 1.819e-03 30.779 < 2e-16 ***
## mean_hr.0 3.464e-02 1.754e-03 19.745 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9951 on 340889 degrees of freedom
## Multiple R-squared: 0.009794, Adjusted R-squared: 0.009765
## F-statistic: 337.2 on 10 and 340889 DF, p-value: < 2.2e-16car::vif(mdl_fit2_hes)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.268498 1.723945 1.955645 1.180631
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.055064 1.235423 1.204583 1.089335
## f.21001.0.0 mean_hr.0
## 1.139237 1.059392# calculate delta adj. r squared
summary(mdl_fit2_hes)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.00134025# plot and nicer summary
summ(mdl_fit2_hes, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 340900
## Dependent Variable: hes_depr.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(10,340889) = 337.177, p = 0.000
## R² = 0.010
## Adj. R² = 0.010
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 11 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.003 0.003 -0.000 1.000
## mean_sbp.0 -0.040 -0.043 -0.036 -20.608 0.000
## high_bp.0 0.009 0.005 0.014 4.237 0.000
## htn_meds_count.0 0.008 0.004 0.013 3.461 0.001
## f.21003.0.0 -0.024 -0.027 -0.020 -12.733 0.000
## f.31.0.0 -0.039 -0.043 -0.036 -22.373 0.000
## angina.0 0.025 0.022 0.029 13.361 0.000
## heartattack.0 0.011 0.007 0.014 5.660 0.000
## f.2443.0.0 0.008 0.004 0.011 4.411 0.000
## f.21001.0.0 0.056 0.052 0.060 30.779 0.000
## mean_hr.0 0.035 0.031 0.038 19.745 0.000
## -------------------------------------------------------------------# forest plots
# main results from replication
cfs = c("Systolic blood pressure" = "mean_sbp.0",
"Diagnosed hypertension" = "high_bp.0",
"No. antihypertensive medication" = "htn_meds_count.0",
"Age" = "f.21003.0.0",
"Gender" = "f.31.0.0",
"Angina pectoris" = "angina.0",
"Heart attack" = "heartattack.0",
"Diabetes" = "f.2443.0.0",
"BMI" = "f.21001.0.0",
"Heart rate" = "mean_hr.0")
plot_summs(mdl_fit2_hes,
coefs = cfs, model.names = c("Hospital-diagnosed Depression"),
legend.title = "Outcome",
colors = "Qual1") + labs(x="Standardized Beta") + xlim(c(-0.1, 0.1)) +
theme(text = element_text(size=16),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position = "top"
)
ggsave("forest_plot_hes_depr.0.png", width = 14, height = 5, device='png', dpi=600)3. Cross-sectional model with HTN outcome from HES
### HTN from HES
### prepare data
dat <- dplyr::select(ukb_lbl, f.eid, depr_c.0, wb.0, mean_sbp.0, hes_htn.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- c("mean_sbp.0", "hes_htn.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, depr_c.0, mean_sbp.0, hes_htn.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5356 -0.5959 -0.2749 0.3707 5.1654
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.828e-15 1.697e-03 0.00 1
## f.21003.0.0 -1.690e-01 1.722e-03 -98.13 <2e-16 ***
## f.31.0.0 -5.334e-02 1.728e-03 -30.86 <2e-16 ***
## angina.0 6.602e-02 1.821e-03 36.26 <2e-16 ***
## heartattack.0 2.463e-02 1.816e-03 13.56 <2e-16 ***
## f.2443.0.0 3.176e-02 1.747e-03 18.18 <2e-16 ***
## depr_l.0 2.662e-01 1.707e-03 155.95 <2e-16 ***
## f.21001.0.0 7.036e-02 1.753e-03 40.14 <2e-16 ***
## mean_hr.0 5.221e-02 1.740e-03 30.01 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9352 on 303762 degrees of freedom
## Multiple R-squared: 0.1254, Adjusted R-squared: 0.1254
## F-statistic: 5446 on 8 and 303762 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit1_hes <- lm(mdl, data=dat_scaled)
summary(mdl_fit1_hes)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5549 -0.5942 -0.2708 0.3685 5.1953
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.008e-15 1.691e-03 0.000 1
## mean_sbp.0 -6.389e-02 1.862e-03 -34.307 < 2e-16 ***
## hes_htn.0 6.863e-02 2.053e-03 33.428 < 2e-16 ***
## htn_meds_count.0 -1.051e-02 2.167e-03 -4.849 1.24e-06 ***
## f.21003.0.0 -1.627e-01 1.851e-03 -87.898 < 2e-16 ***
## f.31.0.0 -4.750e-02 1.742e-03 -27.273 < 2e-16 ***
## angina.0 5.826e-02 1.870e-03 31.162 < 2e-16 ***
## heartattack.0 1.781e-02 1.847e-03 9.641 < 2e-16 ***
## f.2443.0.0 2.431e-02 1.770e-03 13.733 < 2e-16 ***
## depr_l.0 2.627e-01 1.704e-03 154.145 < 2e-16 ***
## f.21001.0.0 6.944e-02 1.811e-03 38.346 < 2e-16 ***
## mean_hr.0 5.436e-02 1.741e-03 31.229 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9321 on 303759 degrees of freedom
## Multiple R-squared: 0.1312, Adjusted R-squared: 0.1312
## F-statistic: 4172 on 11 and 303759 DF, p-value: < 2.2e-16car::vif(mdl_fit1_hes)## mean_sbp.0 hes_htn.0 htn_meds_count.0 f.21003.0.0
## 1.212669 1.473786 1.642198 1.198487
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.060615 1.222295 1.192932 1.095825
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.015419 1.146478 1.059254# calculate delta adj. r squared
summary(mdl_fit1_hes)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.005799211### well-being
outcome <- "wb.0"
predictors <- c("mean_sbp.0", "hes_htn.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, wb.0, mean_sbp.0, hes_htn.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2972 -0.5988 0.0220 0.6339 4.1065
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.846e-16 2.656e-03 0.000 1
## f.21003.0.0 1.886e-01 2.691e-03 70.079 <2e-16 ***
## f.31.0.0 -2.475e-02 2.703e-03 -9.158 <2e-16 ***
## angina.0 -5.272e-02 2.825e-03 -18.663 <2e-16 ***
## heartattack.0 -2.639e-02 2.819e-03 -9.362 <2e-16 ***
## f.2443.0.0 -4.253e-02 2.731e-03 -15.575 <2e-16 ***
## depr_l.0 -1.673e-01 2.671e-03 -62.651 <2e-16 ***
## f.21001.0.0 -8.279e-02 2.741e-03 -30.205 <2e-16 ***
## mean_hr.0 -4.938e-02 2.725e-03 -18.122 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.957 on 129867 degrees of freedom
## Multiple R-squared: 0.08414, Adjusted R-squared: 0.08409
## F-statistic: 1491 on 8 and 129867 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit3_hes <- lm(mdl, data=dat_scaled)
summary(mdl_fit3_hes)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3392 -0.5939 0.0226 0.6295 4.1079
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.293e-16 2.648e-03 0.000 1.000
## mean_sbp.0 5.361e-02 2.913e-03 18.401 < 2e-16 ***
## hes_htn.0 -6.769e-02 3.255e-03 -20.797 < 2e-16 ***
## htn_meds_count.0 4.449e-03 3.414e-03 1.303 0.193
## f.21003.0.0 1.856e-01 2.895e-03 64.092 < 2e-16 ***
## f.31.0.0 -2.771e-02 2.718e-03 -10.194 < 2e-16 ***
## angina.0 -4.365e-02 2.900e-03 -15.051 < 2e-16 ***
## heartattack.0 -1.842e-02 2.869e-03 -6.420 1.37e-10 ***
## f.2443.0.0 -3.455e-02 2.767e-03 -12.486 < 2e-16 ***
## depr_l.0 -1.645e-01 2.667e-03 -61.675 < 2e-16 ***
## f.21001.0.0 -7.910e-02 2.839e-03 -27.862 < 2e-16 ***
## mean_hr.0 -5.071e-02 2.726e-03 -18.601 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9544 on 129864 degrees of freedom
## Multiple R-squared: 0.08922, Adjusted R-squared: 0.08914
## F-statistic: 1156 on 11 and 129864 DF, p-value: < 2.2e-16car::vif(mdl_fit3_hes)## mean_sbp.0 hes_htn.0 htn_meds_count.0 f.21003.0.0
## 1.210100 1.510497 1.661521 1.195105
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.053658 1.199286 1.173826 1.091547
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.014285 1.149124 1.059639# calculate delta adj. r squared
summary(mdl_fit3_hes)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.005053682# plot and nicer summary
summ(mdl_fit1_hes, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 303771
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,303759) = 4171.788, p = 0.000
## R² = 0.131
## Adj. R² = 0.131
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.003 0.003 -0.000 1.000
## mean_sbp.0 -0.064 -0.068 -0.060 -34.307 0.000
## hes_htn.0 0.069 0.065 0.073 33.428 0.000
## htn_meds_count.0 -0.011 -0.015 -0.006 -4.849 0.000
## f.21003.0.0 -0.163 -0.166 -0.159 -87.898 0.000
## f.31.0.0 -0.047 -0.051 -0.044 -27.273 0.000
## angina.0 0.058 0.055 0.062 31.162 0.000
## heartattack.0 0.018 0.014 0.021 9.641 0.000
## f.2443.0.0 0.024 0.021 0.028 13.733 0.000
## depr_l.0 0.263 0.259 0.266 154.145 0.000
## f.21001.0.0 0.069 0.066 0.073 38.346 0.000
## mean_hr.0 0.054 0.051 0.058 31.229 0.000
## -------------------------------------------------------------------summ(mdl_fit3_hes, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 129876
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,129864) = 1156.469, p = 0.000
## R² = 0.089
## Adj. R² = 0.089
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.005 0.005 0.000 1.000
## mean_sbp.0 0.054 0.048 0.059 18.401 0.000
## hes_htn.0 -0.068 -0.074 -0.061 -20.797 0.000
## htn_meds_count.0 0.004 -0.002 0.011 1.303 0.193
## f.21003.0.0 0.186 0.180 0.191 64.092 0.000
## f.31.0.0 -0.028 -0.033 -0.022 -10.194 0.000
## angina.0 -0.044 -0.049 -0.038 -15.051 0.000
## heartattack.0 -0.018 -0.024 -0.013 -6.420 0.000
## f.2443.0.0 -0.035 -0.040 -0.029 -12.486 0.000
## depr_l.0 -0.164 -0.170 -0.159 -61.675 0.000
## f.21001.0.0 -0.079 -0.085 -0.074 -27.862 0.000
## mean_hr.0 -0.051 -0.056 -0.045 -18.601 0.000
## -------------------------------------------------------------------# forest plots
# main results from replication
cfs = c("Systolic blood pressure" = "mean_sbp.0",
"HES Diagnosed hypertension" = "hes_htn.0",
"No. antihypertensive medication" = "htn_meds_count.0",
"Age" = "f.21003.0.0",
"Gender" = "f.31.0.0",
"Angina pectoris" = "angina.0",
"Heart attack" = "heartattack.0",
"Diabetes" = "f.2443.0.0",
"Lifetime depression" = "depr_l.0",
"BMI" = "f.21001.0.0",
"Heart rate" = "mean_hr.0")
plot_summs(mdl_fit1_hes, mdl_fit3_hes,
coefs = cfs, model.names = c("Depressive symptoms (baseline)", "Well-being (baseline)"),
legend.title = "Outcomes",
colors = "Qual1") + labs(x="Standardized Beta") + xlim(c(-0.2, 0.3)) +
theme(text = element_text(size=16),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14),
legend.position = "top"
)
ggsave("forest_plot_hes.0.png", width = 14, height = 5, device='png', dpi=600)Survival bias assessment using death records
# find first date of baseline and last date of follow-up
min(ukb$f.53.0.0, na.rm = T) # "2006-03-13"## [1] "2006-03-13"max(ukb$f.53.2.0, na.rm = T) # "2020-02-09"## [1] "2020-02-09"sum(!is.na(ukb$f.53.2.0)) # 47933 follow-ups## [1] 47933sum(!is.na(ukb$f.40000.0.0)) # 20442 deaths in total## [1] 20442min(ukb$f.40000.0.0, na.rm = T) # "2006-05-10"## [1] "2006-05-10"max(ukb$f.40000.0.0, na.rm = T) # "2018-02-14"## [1] "2018-02-14"x <- ukb$f.40000.0.0 > ukb$f.53.0.0 # 20442 TRUE meaning 20442 died before the follow-up
table(x)## x
## TRUE
## 20442x <- ukb$f.40000.0.0 < ukb$f.53.2.0 # 81 FALSE meaning 81 died after the follow-up
# check baseline, follow-up and death dates again of those 81
data.frame(ukb$f.53.0.0[!is.na(ukb$f.40000.0.0[x])], ukb$f.53.2.0[!is.na(ukb$f.40000.0.0[x])],ukb$f.40000.0.0[!is.na(ukb$f.40000.0.0[x])])| ukb.f.53.0.0..is.na.ukb.f.40000.0.0.x… | ukb.f.53.2.0..is.na.ukb.f.40000.0.0.x… | ukb.f.40000.0.0..is.na.ukb.f.40000.0.0.x… |
|---|
ukb$f.53.2.0[!is.na(ukb$f.40000.0.0[x])] < ukb$f.40000.0.0[!is.na(ukb$f.40000.0.0[x])]## logical(0)# Are the 20442 who died different from the main sample? And how many of those have had HTN?
ukb$death.0 <- ifelse((ukb$f.40000.0.0 > ukb$f.53.0.0) == T, 1, 0)
dscrp <- dplyr::select(ukb, f.31.0.0, f.738.0.0, f.21000.0.0, f.2443.0.0, f.20116.0.0, f.1558.0.0, f.22040.0.0, f.54.0.0,
f.21003.0.0, f.21003.2.0, f.189.0.0,
angina.0, heartattack.0, f.1200.0.0, education.0, ethn.0,
depr_l.0, mean_hr.0, mean_sbp.0, mean_sbp.2, mean_dbp.0, mean_dbp.2,
f.21001.0.0, htn_meds_count.0, depr_meds_count.0, depr_c.0, depr_c.2, wb.0,
wb.2, phq9.0, high_bp.0, high_bp.2, death.0)
dscrp$angina.0 <- factor(dscrp$angina.0, levels=c(0, 1),
labels=c("No diagnosed angina or unknown", "Diagnosed angina"))
dscrp$heartattack.0 <- factor(dscrp$heartattack.0, levels=c(0, 1),
labels=c("No diagnosed heart attack or unknown",
"Diagnosed heart attack"))
dscrp$depr_l.0 <- factor(dscrp$depr_l.0, levels=c(0, 1),
labels=c("No diagnosed depression or unknown", "Diagnosed depression"))
dscrp$high_bp.0 <- factor(dscrp$high_bp.0, levels=c(0, 1, 2),
labels=c("No diagnosed HTN or unknown", "Diagnosed HTN",
"P-value / Effect size"))
dscrp$high_bp.2 <- factor(dscrp$high_bp.2, levels=c(0, 1),
labels=c("No diagnosed HTN or unknown", "Diagnosed HTN"))
dscrp$death.0 <- factor(dscrp$death.0, levels=c(0, 1),
labels=c("Alive or unknown", "Died"))
# add meaningful labels
label(dscrp$f.31.0.0) <- "Gender"
label(dscrp$f.21003.0.0) <- "Age (years)"
label(dscrp$f.21003.2.0) <- "Age (years)"
#units(ukb$f.21003.0.0) <- "years"
label(dscrp$f.738.0.0) <- "Household income (£)"
label(dscrp$f.189.0.0) <- "Townsend deprivation index"
label(dscrp$f.21000.0.0) <- "Ethnic background"
label(dscrp$f.2443.0.0) <- "Diabetes"
label(dscrp$f.20116.0.0) <- "Smoking"
label(dscrp$f.1558.0.0) <- "Alcohol consumption"
label(dscrp$f.22040.0.0) <- "Physical activity (minutes/week)"
label(dscrp$angina.0) <- "Angina"
label(dscrp$heartattack.0) <- "Heart attack"
label(dscrp$depr_l.0) <- "Lifetime depression"
label(dscrp$mean_hr.0) <- "Heart rate (beats/min)"
label(dscrp$mean_sbp.0) <- "Systolic blood pressure (mmHg)"
label(dscrp$mean_sbp.2) <- "Systolic blood pressure (mmHg)"
label(dscrp$mean_dbp.0) <- "Diastolic blood pressure (mmHg)"
label(dscrp$mean_dbp.2) <- "Diastolic blood pressure (mmHg)"
label(dscrp$f.21001.0.0) <- "BMI (kg/m2)"
label(dscrp$htn_meds_count.0) <- "No. antihypertensive medication"
label(dscrp$depr_meds_count.0) <- "No. antidepressant medication"
label(dscrp$depr_c.0) <- "Current depressive symptoms"
label(dscrp$depr_c.2) <- "Current depressive symptoms (2nd follow-up)"
label(dscrp$wb.0) <- "Well-being"
label(dscrp$wb.2) <- "Well-being (2nd follow-up)"
label(dscrp$phq9.0) <- "PHQ-9 depressive symptoms (1st follow-up)"
label(dscrp$high_bp.0) <- "Diagnosed hypertension"
label(dscrp$high_bp.2) <- "Diagnosed hypertension (2nd follow-up)"
#descriptives
table1(~ f.31.0.0 + f.21003.0.0 + f.738.0.0 + f.189.0.0 + f.21000.0.0 + f.2443.0.0 + f.20116.0.0 +
f.1558.0.0 + f.22040.0.0 + angina.0 + heartattack.0 + depr_l.0 + mean_hr.0 + mean_sbp.0 +
mean_dbp.0 + f.21001.0.0 + htn_meds_count.0 + depr_meds_count.0 + depr_c.0 + depr_c.2 +
wb.0 + wb.2 + phq9.0 + high_bp.0 | death.0, data=dscrp)| Died (N=20442) |
Overall (N=502494) |
|
|---|---|---|
| Gender | ||
| Female | 8116 (39.7%) | 273378 (54.4%) |
| Male | 12326 (60.3%) | 229115 (45.6%) |
| Missing | 0 (0%) | 1 (0.0%) |
| Age (years) | ||
| Mean (SD) | 61.4 (6.55) | 56.5 (8.10) |
| Median [Min, Max] | 63.0 [40.0, 70.0] | 58.0 [37.0, 73.0] |
| Missing | 0 (0%) | 1 (0.0%) |
| Household income (£) | ||
| Prefer not to answer | 2214 (10.8%) | 49844 (9.9%) |
| Do not know | 1265 (6.2%) | 21304 (4.2%) |
| Less than 18,000 | 6760 (33.1%) | 97196 (19.3%) |
| 18,000 to 30,999 | 4678 (22.9%) | 108174 (21.5%) |
| 31,000 to 51,999 | 2990 (14.6%) | 110771 (22.0%) |
| 52,000 to 100,000 | 1644 (8.0%) | 86263 (17.2%) |
| Greater than 100,000 | 371 (1.8%) | 22929 (4.6%) |
| Missing | 520 (2.5%) | 6013 (1.2%) |
| Townsend deprivation index | ||
| Mean (SD) | -0.658 (3.42) | -1.29 (3.10) |
| Median [Min, Max] | -1.55 [-6.26, 10.9] | -2.14 [-6.26, 11.0] |
| Missing | 20 (0.1%) | 624 (0.1%) |
| Ethnic background | ||
| Prefer not to answer | 101 (0.5%) | 1661 (0.3%) |
| Do not know | 11 (0.1%) | 217 (0.0%) |
| White | 32 (0.2%) | 570 (0.1%) |
| Mixed | 3 (0.0%) | 49 (0.0%) |
| Asian or Asian British | 1 (0.0%) | 43 (0.0%) |
| Black or Black British | 0 (0%) | 27 (0.0%) |
| Chinese | 32 (0.2%) | 1574 (0.3%) |
| Other ethnic group | 132 (0.6%) | 4558 (0.9%) |
| British | 18449 (90.3%) | 442575 (88.1%) |
| Irish | 618 (3.0%) | 13207 (2.6%) |
| Any other white background | 498 (2.4%) | 16332 (3.3%) |
| White and Black Caribbean | 14 (0.1%) | 620 (0.1%) |
| White and Black African | 11 (0.1%) | 425 (0.1%) |
| White and Asian | 17 (0.1%) | 831 (0.2%) |
| Any other mixed background | 31 (0.2%) | 1033 (0.2%) |
| Indian | 174 (0.9%) | 5951 (1.2%) |
| Pakistani | 49 (0.2%) | 1837 (0.4%) |
| Bangladeshi | 9 (0.0%) | 236 (0.0%) |
| Any other Asian background | 30 (0.1%) | 1815 (0.4%) |
| Caribbean | 107 (0.5%) | 4517 (0.9%) |
| African | 77 (0.4%) | 3394 (0.7%) |
| Any other Black background | 1 (0.0%) | 123 (0.0%) |
| Missing | 45 (0.2%) | 899 (0.2%) |
| Diabetes | ||
| Prefer not to answer | 21 (0.1%) | 404 (0.1%) |
| Do not know | 81 (0.4%) | 1280 (0.3%) |
| No | 17592 (86.1%) | 473479 (94.2%) |
| Yes | 2700 (13.2%) | 26399 (5.3%) |
| Missing | 48 (0.2%) | 932 (0.2%) |
| Smoking | ||
| Prefer not to answer | 152 (0.7%) | 2057 (0.4%) |
| Never | 7567 (37.0%) | 273517 (54.4%) |
| Previous | 8542 (41.8%) | 173051 (34.4%) |
| Current | 4136 (20.2%) | 52977 (10.5%) |
| Missing | 45 (0.2%) | 892 (0.2%) |
| Alcohol consumption | ||
| Prefer not to answer | 48 (0.2%) | 604 (0.1%) |
| Daily or almost daily | 4682 (22.9%) | 101768 (20.3%) |
| Three or four times a week | 3825 (18.7%) | 115435 (23.0%) |
| Once or twice a week | 4681 (22.9%) | 129289 (25.7%) |
| One to three times a month | 1902 (9.3%) | 55855 (11.1%) |
| Special occasions only | 2801 (13.7%) | 58006 (11.5%) |
| Never | 2458 (12.0%) | 40639 (8.1%) |
| Missing | 45 (0.2%) | 898 (0.2%) |
| Physical activity (minutes/week) | ||
| Mean (SD) | 2450 (2740) | 2650 (2710) |
| Median [Min, Max] | 1520 [0, 19300] | 1770 [0, 19300] |
| Missing | 4872 (23.8%) | 100122 (19.9%) |
| Angina | ||
| No diagnosed angina or unknown | 15513 (75.9%) | 358910 (71.4%) |
| Diagnosed angina | 1883 (9.2%) | 16117 (3.2%) |
| Missing | 3046 (14.9%) | 127467 (25.4%) |
| Heart attack | ||
| No diagnosed heart attack or unknown | 15762 (77.1%) | 363524 (72.3%) |
| Diagnosed heart attack | 1634 (8.0%) | 11503 (2.3%) |
| Missing | 3046 (14.9%) | 127467 (25.4%) |
| Lifetime depression | ||
| No diagnosed depression or unknown | 16028 (78.4%) | 346919 (69.0%) |
| Diagnosed depression | 1368 (6.7%) | 28108 (5.6%) |
| Missing | 3046 (14.9%) | 127467 (25.4%) |
| Heart rate (beats/min) | ||
| Mean (SD) | 72.1 (12.8) | 69.3 (11.2) |
| Median [Min, Max] | 71.0 [33.5, 148] | 68.5 [30.5, 173] |
| Missing | 2305 (11.3%) | 45528 (9.1%) |
| Systolic blood pressure (mmHg) | ||
| Mean (SD) | 142 (19.9) | 138 (18.6) |
| Median [Min, Max] | 141 [76.5, 254] | 136 [65.0, 254] |
| Missing | 2307 (11.3%) | 45540 (9.1%) |
| Diastolic blood pressure (mmHg) | ||
| Mean (SD) | 82.1 (10.8) | 82.2 (10.1) |
| Median [Min, Max] | 82.0 [36.5, 133] | 82.0 [36.5, 148] |
| Missing | 2305 (11.3%) | 45528 (9.1%) |
| BMI (kg/m2) | ||
| Mean (SD) | 28.2 (5.44) | 27.4 (4.80) |
| Median [Min, Max] | 27.4 [12.8, 74.7] | 26.7 [12.1, 74.7] |
| Missing | 328 (1.6%) | 3105 (0.6%) |
| No. antihypertensive medication | ||
| Mean (SD) | 0.852 (1.25) | 0.403 (0.864) |
| Median [Min, Max] | 0 [0, 9.00] | 0 [0, 9.00] |
| No. antidepressant medication | ||
| Mean (SD) | 0.120 (0.346) | 0.0784 (0.281) |
| Median [Min, Max] | 0 [0, 3.00] | 0 [0, 5.00] |
| Current depressive symptoms | ||
| Mean (SD) | 1.48 (0.599) | 1.40 (0.528) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] |
| Missing | 2662 (13.0%) | 53563 (10.7%) |
| Current depressive symptoms (2nd follow-up) | ||
| Mean (SD) | 1.31 (0.393) | 1.30 (0.438) |
| Median [Min, Max] | 1.25 [1.00, 2.75] | 1.25 [1.00, 4.00] |
| Missing | 20372 (99.7%) | 457846 (91.1%) |
| Well-being | ||
| Mean (SD) | 4.37 (0.620) | 4.46 (0.579) |
| Median [Min, Max] | 4.40 [1.00, 6.00] | 4.50 [1.00, 6.00] |
| Missing | 14704 (71.9%) | 330042 (65.7%) |
| Well-being (2nd follow-up) | ||
| Mean (SD) | 4.58 (0.480) | 4.63 (0.539) |
| Median [Min, Max] | 4.60 [3.67, 5.60] | 4.67 [1.00, 6.00] |
| Missing | 20364 (99.6%) | 454932 (90.5%) |
| PHQ-9 depressive symptoms (1st follow-up) | ||
| Mean (SD) | 4.30 (4.82) | 2.76 (3.70) |
| Median [Min, Max] | 3.00 [0, 25.0] | 2.00 [0, 27.0] |
| Missing | 19852 (97.1%) | 348149 (69.3%) |
| Diagnosed hypertension | ||
| No diagnosed HTN or unknown | 12070 (59.0%) | 365819 (72.8%) |
| Diagnosed HTN | 8324 (40.7%) | 135745 (27.0%) |
| P-value / Effect size | 0 (0%) | 0 (0%) |
| Missing | 48 (0.2%) | 930 (0.2%) |
# shortened descriptives, baseline
table1(~ f.31.0.0 + f.21003.0.0 + f.189.0.0 + mean_sbp.0 +
mean_dbp.0 + mean_hr.0 + f.21001.0.0 + f.2443.0.0 +
angina.0 + heartattack.0 + depr_l.0 + htn_meds_count.0 + depr_meds_count.0 +
depr_c.0 + wb.0 + high_bp.0 | death.0, data=dscrp)| Died (N=20442) |
Overall (N=502494) |
|
|---|---|---|
| Gender | ||
| Female | 8116 (39.7%) | 273378 (54.4%) |
| Male | 12326 (60.3%) | 229115 (45.6%) |
| Missing | 0 (0%) | 1 (0.0%) |
| Age (years) | ||
| Mean (SD) | 61.4 (6.55) | 56.5 (8.10) |
| Median [Min, Max] | 63.0 [40.0, 70.0] | 58.0 [37.0, 73.0] |
| Missing | 0 (0%) | 1 (0.0%) |
| Townsend deprivation index | ||
| Mean (SD) | -0.658 (3.42) | -1.29 (3.10) |
| Median [Min, Max] | -1.55 [-6.26, 10.9] | -2.14 [-6.26, 11.0] |
| Missing | 20 (0.1%) | 624 (0.1%) |
| Systolic blood pressure (mmHg) | ||
| Mean (SD) | 142 (19.9) | 138 (18.6) |
| Median [Min, Max] | 141 [76.5, 254] | 136 [65.0, 254] |
| Missing | 2307 (11.3%) | 45540 (9.1%) |
| Diastolic blood pressure (mmHg) | ||
| Mean (SD) | 82.1 (10.8) | 82.2 (10.1) |
| Median [Min, Max] | 82.0 [36.5, 133] | 82.0 [36.5, 148] |
| Missing | 2305 (11.3%) | 45528 (9.1%) |
| Heart rate (beats/min) | ||
| Mean (SD) | 72.1 (12.8) | 69.3 (11.2) |
| Median [Min, Max] | 71.0 [33.5, 148] | 68.5 [30.5, 173] |
| Missing | 2305 (11.3%) | 45528 (9.1%) |
| BMI (kg/m2) | ||
| Mean (SD) | 28.2 (5.44) | 27.4 (4.80) |
| Median [Min, Max] | 27.4 [12.8, 74.7] | 26.7 [12.1, 74.7] |
| Missing | 328 (1.6%) | 3105 (0.6%) |
| Diabetes | ||
| Prefer not to answer | 21 (0.1%) | 404 (0.1%) |
| Do not know | 81 (0.4%) | 1280 (0.3%) |
| No | 17592 (86.1%) | 473479 (94.2%) |
| Yes | 2700 (13.2%) | 26399 (5.3%) |
| Missing | 48 (0.2%) | 932 (0.2%) |
| Angina | ||
| No diagnosed angina or unknown | 15513 (75.9%) | 358910 (71.4%) |
| Diagnosed angina | 1883 (9.2%) | 16117 (3.2%) |
| Missing | 3046 (14.9%) | 127467 (25.4%) |
| Heart attack | ||
| No diagnosed heart attack or unknown | 15762 (77.1%) | 363524 (72.3%) |
| Diagnosed heart attack | 1634 (8.0%) | 11503 (2.3%) |
| Missing | 3046 (14.9%) | 127467 (25.4%) |
| Lifetime depression | ||
| No diagnosed depression or unknown | 16028 (78.4%) | 346919 (69.0%) |
| Diagnosed depression | 1368 (6.7%) | 28108 (5.6%) |
| Missing | 3046 (14.9%) | 127467 (25.4%) |
| No. antihypertensive medication | ||
| Mean (SD) | 0.852 (1.25) | 0.403 (0.864) |
| Median [Min, Max] | 0 [0, 9.00] | 0 [0, 9.00] |
| No. antidepressant medication | ||
| Mean (SD) | 0.120 (0.346) | 0.0784 (0.281) |
| Median [Min, Max] | 0 [0, 3.00] | 0 [0, 5.00] |
| Current depressive symptoms | ||
| Mean (SD) | 1.48 (0.599) | 1.40 (0.528) |
| Median [Min, Max] | 1.25 [1.00, 4.00] | 1.25 [1.00, 4.00] |
| Missing | 2662 (13.0%) | 53563 (10.7%) |
| Well-being | ||
| Mean (SD) | 4.37 (0.620) | 4.46 (0.579) |
| Median [Min, Max] | 4.40 [1.00, 6.00] | 4.50 [1.00, 6.00] |
| Missing | 14704 (71.9%) | 330042 (65.7%) |
| Diagnosed hypertension | ||
| No diagnosed HTN or unknown | 12070 (59.0%) | 365819 (72.8%) |
| Diagnosed HTN | 8324 (40.7%) | 135745 (27.0%) |
| P-value / Effect size | 0 (0%) | 0 (0%) |
| Missing | 48 (0.2%) | 930 (0.2%) |
# Assess cross-sectional associations in non-surviving sample
### prepare data
dat <- ukb %>% filter(., death.0 == 1) %>% dplyr::select(., f.eid, depr_c.0, wb.0, mean_sbp.0, high_bp.0, htn_meds_count.0,
f.21003.0.0, f.31.0.0, angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0)
dat$f.31.0.0 <- as.numeric(dat$f.31.0.0)
dat$f.2443.0.0 <- as.numeric(dat$f.2443.0.0)
# define predictors and covariates
predictors <- c("mean_sbp.0", "high_bp.0", "htn_meds_count.0")
covs <- c("f.21003.0.0", "f.31.0.0", "angina.0", "heartattack.0", "f.2443.0.0", "depr_l.0",
"f.21001.0.0", "mean_hr.0")
### depressive mood
outcome <- "depr_c.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, depr_c.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4124 -0.6174 -0.2680 0.3991 4.4730
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.033e-16 8.111e-03 0.000 1.000000
## f.21003.0.0 -1.545e-01 8.254e-03 -18.717 < 2e-16 ***
## f.31.0.0 -4.078e-02 8.276e-03 -4.927 8.43e-07 ***
## angina.0 9.589e-02 9.113e-03 10.522 < 2e-16 ***
## heartattack.0 2.119e-02 9.106e-03 2.327 0.019965 *
## f.2443.0.0 3.757e-02 8.534e-03 4.403 1.08e-05 ***
## depr_l.0 2.613e-01 8.179e-03 31.953 < 2e-16 ***
## f.21001.0.0 3.115e-02 8.393e-03 3.711 0.000207 ***
## mean_hr.0 9.194e-02 8.267e-03 11.122 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9345 on 13268 degrees of freedom
## Multiple R-squared: 0.1271, Adjusted R-squared: 0.1266
## F-statistic: 241.6 on 8 and 13268 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_dead <- lm(mdl, data=dat_scaled)
summary(mdl_fit_dead)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3822 -0.6169 -0.2704 0.3982 4.4286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.419e-16 8.086e-03 0.000 1.000000
## mean_sbp.0 -7.311e-02 8.701e-03 -8.402 < 2e-16 ***
## high_bp.0 4.729e-02 9.981e-03 4.738 2.19e-06 ***
## htn_meds_count.0 -3.623e-03 1.132e-02 -0.320 0.749005
## f.21003.0.0 -1.432e-01 8.484e-03 -16.876 < 2e-16 ***
## f.31.0.0 -3.654e-02 8.306e-03 -4.399 1.09e-05 ***
## angina.0 8.888e-02 9.506e-03 9.349 < 2e-16 ***
## heartattack.0 1.522e-02 9.387e-03 1.622 0.104876
## f.2443.0.0 3.147e-02 8.667e-03 3.631 0.000284 ***
## depr_l.0 2.582e-01 8.164e-03 31.629 < 2e-16 ***
## f.21001.0.0 3.201e-02 8.638e-03 3.706 0.000211 ***
## mean_hr.0 9.170e-02 8.271e-03 11.087 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9317 on 13265 degrees of freedom
## Multiple R-squared: 0.1327, Adjusted R-squared: 0.1319
## F-statistic: 184.5 on 11 and 13265 DF, p-value: < 2.2e-16car::vif(mdl_fit_dead)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.157979 1.523603 1.961417 1.100890
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.055172 1.382143 1.347628 1.148955
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.019260 1.141122 1.046239# calculate delta adj. r squared
summary(mdl_fit_dead)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.005329516### well-being
outcome <- "wb.0"
dat_scaled <- data.frame(scale(na.omit(dplyr::select(dat, wb.0, mean_sbp.0, high_bp.0,
htn_meds_count.0, f.21003.0.0, f.31.0.0,
angina.0, heartattack.0, f.2443.0.0, depr_l.0,
f.21001.0.0, mean_hr.0))))
# null model (only covariates)
mdl <- as.formula(paste(paste(outcome), paste(covs, collapse = " + "), sep = " ~ "))
mdl_fit <- lm(mdl, data=dat_scaled)
summary(mdl_fit)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9131 -0.5743 0.0398 0.6301 3.0832
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.317e-16 1.365e-02 0.000 1.00000
## f.21003.0.0 2.050e-01 1.388e-02 14.766 < 2e-16 ***
## f.31.0.0 -3.600e-02 1.394e-02 -2.583 0.00983 **
## angina.0 -7.896e-02 1.514e-02 -5.217 1.90e-07 ***
## heartattack.0 -1.490e-02 1.507e-02 -0.988 0.32296
## f.2443.0.0 -7.196e-02 1.447e-02 -4.974 6.79e-07 ***
## depr_l.0 -1.615e-01 1.375e-02 -11.744 < 2e-16 ***
## f.21001.0.0 -9.251e-03 1.417e-02 -0.653 0.51372
## mean_hr.0 -6.756e-02 1.391e-02 -4.858 1.22e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9523 on 4856 degrees of freedom
## Multiple R-squared: 0.09453, Adjusted R-squared: 0.09304
## F-statistic: 63.37 on 8 and 4856 DF, p-value: < 2.2e-16# with predictors
mdl <- as.formula(paste(paste(outcome), paste(paste(predictors, collapse = " + "),
paste(covs, collapse = " + "), sep = " + "),
sep = " ~ "))
mdl_fit_dead_wb.0 <- lm(mdl, data=dat_scaled)
summary(mdl_fit_dead_wb.0)##
## Call:
## lm(formula = mdl, data = dat_scaled)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9608 -0.5844 0.0525 0.6281 3.1095
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.386e-16 1.362e-02 0.000 1.0000
## mean_sbp.0 6.320e-02 1.456e-02 4.342 1.44e-05 ***
## high_bp.0 -1.578e-02 1.689e-02 -0.934 0.3501
## htn_meds_count.0 -4.013e-02 1.910e-02 -2.101 0.0357 *
## f.21003.0.0 1.983e-01 1.430e-02 13.864 < 2e-16 ***
## f.31.0.0 -3.569e-02 1.400e-02 -2.549 0.0108 *
## angina.0 -6.195e-02 1.584e-02 -3.912 9.28e-05 ***
## heartattack.0 -1.301e-03 1.546e-02 -0.084 0.9329
## f.2443.0.0 -6.300e-02 1.466e-02 -4.297 1.77e-05 ***
## depr_l.0 -1.582e-01 1.374e-02 -11.521 < 2e-16 ***
## f.21001.0.0 -8.908e-03 1.460e-02 -0.610 0.5419
## mean_hr.0 -6.839e-02 1.392e-02 -4.914 9.22e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9499 on 4853 degrees of freedom
## Multiple R-squared: 0.09975, Adjusted R-squared: 0.09771
## F-statistic: 48.88 on 11 and 4853 DF, p-value: < 2.2e-16car::vif(mdl_fit_dead_wb.0)## mean_sbp.0 high_bp.0 htn_meds_count.0 f.21003.0.0
## 1.142259 1.537679 1.965630 1.103108
## f.31.0.0 angina.0 heartattack.0 f.2443.0.0
## 1.056465 1.351951 1.288613 1.158912
## depr_l.0 f.21001.0.0 mean_hr.0
## 1.017071 1.149794 1.044271# calculate delta adj. r squared
summary(mdl_fit_dead_wb.0)$adj.r.squared - summary(mdl_fit)$adj.r.squared## [1] 0.004673925# plot and nicer summary
summ(mdl_fit_dead, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 13277
## Dependent Variable: depr_c.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,13265) = 184.452, p = 0.000
## R² = 0.133
## Adj. R² = 0.132
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) 0.000 -0.016 0.016 0.000 1.000
## mean_sbp.0 -0.073 -0.090 -0.056 -8.402 0.000
## high_bp.0 0.047 0.028 0.067 4.738 0.000
## htn_meds_count.0 -0.004 -0.026 0.019 -0.320 0.749
## f.21003.0.0 -0.143 -0.160 -0.127 -16.876 0.000
## f.31.0.0 -0.037 -0.053 -0.020 -4.399 0.000
## angina.0 0.089 0.070 0.108 9.349 0.000
## heartattack.0 0.015 -0.003 0.034 1.622 0.105
## f.2443.0.0 0.031 0.014 0.048 3.631 0.000
## depr_l.0 0.258 0.242 0.274 31.629 0.000
## f.21001.0.0 0.032 0.015 0.049 3.706 0.000
## mean_hr.0 0.092 0.075 0.108 11.087 0.000
## -------------------------------------------------------------------summ(mdl_fit_dead_wb.0, confint = TRUE, digits = 3)## MODEL INFO:
## Observations: 4865
## Dependent Variable: wb.0
## Type: OLS linear regression
##
## MODEL FIT:
## F(11,4853) = 48.885, p = 0.000
## R² = 0.100
## Adj. R² = 0.098
##
## Standard errors: OLS## Warning in row.names.provided && row.names == FALSE: 'length(x) = 12 > 1' in
## coercion to 'logical(1)'## -------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## ---------------------- -------- -------- -------- --------- -------
## (Intercept) -0.000 -0.027 0.027 -0.000 1.000
## mean_sbp.0 0.063 0.035 0.092 4.342 0.000
## high_bp.0 -0.016 -0.049 0.017 -0.934 0.350
## htn_meds_count.0 -0.040 -0.078 -0.003 -2.101 0.036
## f.21003.0.0 0.198 0.170 0.226 13.864 0.000
## f.31.0.0 -0.036 -0.063 -0.008 -2.549 0.011
## angina.0 -0.062 -0.093 -0.031 -3.912 0.000
## heartattack.0 -0.001 -0.032 0.029 -0.084 0.933
## f.2443.0.0 -0.063 -0.092 -0.034 -4.297 0.000
## depr_l.0 -0.158 -0.185 -0.131 -11.521 0.000
## f.21001.0.0 -0.009 -0.038 0.020 -0.610 0.542
## mean_hr.0 -0.068 -0.096 -0.041 -4.914 0.000
## -------------------------------------------------------------------Linear Mixed Effects analysis
library(lme4)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack##
## Attaching package: 'lme4'## The following object is masked from 'package:expss':
##
## dummy# depressive symptoms
d_long <- dplyr::select(ukb_lbl, f.eid, depr_c.0, depr_c.2, mean_sbp.0, mean_sbp.2, high_bp.0, high_bp.2,
htn_meds_count.0, htn_meds_count.2, f.21003.0.0, f.31.0.0, angina.0, heartattack.0,
f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0)
d_long <- unlab(d_long)
d_long <- d_long %>%
pivot_longer(
cols = c(2:9),
names_to = c(".value","assessment"),
names_pattern = "(.*)(.)$",
values_drop_na = FALSE)
d_long <- d_long %>% group_by(f.eid) %>% filter(n()>1)
# null model
lmeFit.p0 <- lmer(depr_c. ~ assessment + mean_sbp. + high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long)
# full model
lmeFit.p <- lmer(depr_c. ~ assessment*mean_sbp. + assessment*high_bp. + mean_sbp.*high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long)
anova(lmeFit.p0,lmeFit.p) # p=0.01367## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0 | 17 | 464499.4 | 464680.4 | -232232.7 | 464465.4 | NA | NA | NA |
| lmeFit.p | 20 | 464494.8 | 464707.7 | -232227.4 | 464454.8 | 10.66692 | 3 | 0.0136703 |
#drop1(lmeFit.p, test="Chisq")
# test which interactions are significant
lmeFit.p0.1 <- lmer(depr_c. ~ assessment*mean_sbp. + assessment*high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long)
anova(lmeFit.p0.1,lmeFit.p) # sbp*high_bp ns## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0.1 | 19 | 464492.8 | 464695.0 | -232227.4 | 464454.8 | NA | NA | NA |
| lmeFit.p | 20 | 464494.8 | 464707.7 | -232227.4 | 464454.8 | 0.0010812 | 1 | 0.9737685 |
lmeFit.p0.2 <- lmer(depr_c. ~ assessment*mean_sbp. + mean_sbp.*high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long)
anova(lmeFit.p0.2,lmeFit.p) # high_bp*time ns## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0.2 | 19 | 464494.5 | 464696.8 | -232228.3 | 464456.5 | NA | NA | NA |
| lmeFit.p | 20 | 464494.8 | 464707.7 | -232227.4 | 464454.8 | 1.778753 | 1 | 0.1823026 |
lmeFit.p0.3 <- lmer(depr_c. ~ assessment*high_bp. + mean_sbp.*high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long)
anova(lmeFit.p0.3,lmeFit.p) # sbp*time p=0.00116## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0.3 | 19 | 464503.3 | 464705.6 | -232232.7 | 464465.3 | NA | NA | NA |
| lmeFit.p | 20 | 464494.8 | 464707.7 | -232227.4 | 464454.8 | 10.55262 | 1 | 0.0011602 |
# reduced full model to test main effect of HTN
d_long_2 = d_long[!is.na(d_long$high_bp.),]
lmeFit.p.1 <- lmer(depr_c. ~ mean_sbp.*assessment + high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long_2)
# reduced null model
lmeFit.p0.4 <- lmer(depr_c. ~ mean_sbp.*assessment + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long_2)
anova(lmeFit.p.1, lmeFit.p0.4) # main effect HTN p< 2.2e-16## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0.4 | 17 | 464849.2 | 465030.2 | -232407.6 | 464815.2 | NA | NA | NA |
| lmeFit.p.1 | 18 | 464492.5 | 464684.2 | -232228.3 | 464456.5 | 358.6895 | 1 | 0 |
summary(lmeFit.p.1)## Linear mixed model fit by REML ['lmerMod']
## Formula: depr_c. ~ mean_sbp. * assessment + high_bp. + htn_meds_count. +
## f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 + f.2443.0.0 +
## depr_l.0 + f.21001.0.0 + mean_hr.0 + (1 | f.eid)
## Data: d_long_2
##
## REML criterion at convergence: 464638.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1236 -0.4041 -0.1819 0.2529 5.6596
##
## Random effects:
## Groups Name Variance Std.Dev.
## f.eid (Intercept) 0.1578 0.3973
## Residual 0.1056 0.3250
## Number of obs: 310796, groups: f.eid, 304167
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2.027e+00 1.987e-02 102.013
## mean_sbp. -1.842e-03 5.584e-05 -32.991
## assessment2 -1.797e-01 3.859e-02 -4.657
## high_bp. 4.785e-02 2.525e-03 18.946
## htn_meds_count. -4.004e-03 1.361e-03 -2.942
## f.21003.0.0 -1.089e-02 1.282e-04 -84.957
## f.31.0.0.L -3.737e-02 1.356e-03 -27.563
## angina.0 1.740e-01 5.232e-03 33.255
## heartattack.0 7.003e-02 6.015e-03 11.643
## f.2443.0.0.L -2.513e-01 4.140e-02 -6.069
## f.2443.0.0.Q 5.233e-02 3.233e-02 1.619
## f.2443.0.0.C 1.528e-01 1.936e-02 7.896
## depr_l.0 5.545e-01 3.575e-03 155.108
## f.21001.0.0 7.962e-03 2.037e-04 39.088
## mean_hr.0 2.563e-03 8.390e-05 30.546
## mean_sbp.:assessment2 8.352e-04 2.802e-04 2.981##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# well-being
d_long <- dplyr::select(ukb_lbl, f.eid, wb.0, wb.2, mean_sbp.0, mean_sbp.2, high_bp.0, high_bp.2,
htn_meds_count.0, htn_meds_count.2, f.21003.0.0, f.31.0.0, angina.0, heartattack.0,
f.2443.0.0, depr_l.0, f.21001.0.0, mean_hr.0)
d_long <- unlab(d_long)
#d_long <- drop_na(d_long)
d_long <- d_long %>%
pivot_longer(
cols = c(2:9),
names_to = c(".value","assessment"),
names_pattern = "(.*)(.)$",
values_drop_na = FALSE)
d_long <- d_long %>% group_by(f.eid) %>% filter(n()>1)
# null model
lmeFit.p0 <- lmer(wb. ~ assessment + mean_sbp. + high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long)
# full model
lmeFit.p <- lmer(wb. ~ assessment*mean_sbp. + assessment*high_bp. + mean_sbp.*high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long)
anova(lmeFit.p0,lmeFit.p) # ns p=0.1452## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0 | 17 | 228352.3 | 228519.4 | -114159.1 | 228318.3 | NA | NA | NA |
| lmeFit.p | 20 | 228352.9 | 228549.5 | -114156.4 | 228312.9 | 5.392157 | 3 | 0.145233 |
# reduced full model to test main effect of HTN
d_long_2 = d_long[!is.na(d_long$high_bp.),]
lmeFit.p.1 <- lmer(wb. ~ mean_sbp.*assessment + high_bp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long_2)
# reduced null model
lmeFit.p0.1 <- lmer(wb. ~ mean_sbp.*assessment + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long_2)
anova(lmeFit.p.1, lmeFit.p0.1) # main effect HTN p< 2.2e-16## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0.1 | 17 | 228608.2 | 228775.4 | -114287.1 | 228574.2 | NA | NA | NA |
| lmeFit.p.1 | 18 | 228353.7 | 228530.7 | -114158.9 | 228317.7 | 256.5365 | 1 | 0 |
summary(lmeFit.p.1)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## wb. ~ mean_sbp. * assessment + high_bp. + htn_meds_count. + f.21003.0.0 +
## f.31.0.0 + angina.0 + heartattack.0 + f.2443.0.0 + depr_l.0 +
## f.21001.0.0 + mean_hr.0 + (1 | f.eid)
## Data: d_long_2
##
## REML criterion at convergence: 228486.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6999 -0.3450 0.0117 0.3640 3.3112
##
## Random effects:
## Groups Name Variance Std.Dev.
## f.eid (Intercept) 0.21957 0.4686
## Residual 0.09339 0.3056
## Number of obs: 137384, groups: f.eid, 134504
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.755e+00 2.455e-02 152.924
## mean_sbp. 1.712e-03 9.223e-05 18.568
## assessment2 1.619e-01 4.380e-02 3.697
## high_bp. -6.649e-02 4.150e-03 -16.025
## htn_meds_count. 4.888e-03 2.239e-03 2.184
## f.21003.0.0 1.319e-02 2.093e-04 63.021
## f.31.0.0.L -2.151e-02 2.221e-03 -9.688
## angina.0 -1.469e-01 8.709e-03 -16.872
## heartattack.0 -7.907e-02 1.001e-02 -7.901
## f.2443.0.0.L 1.986e-01 3.838e-02 5.176
## f.2443.0.0.Q -6.991e-02 3.159e-02 -2.213
## f.2443.0.0.C -1.438e-01 2.285e-02 -6.293
## depr_l.0 -3.761e-01 5.985e-03 -62.852
## f.21001.0.0 -9.436e-03 3.325e-04 -28.379
## mean_hr.0 -2.442e-03 1.391e-04 -17.553
## mean_sbp.:assessment2 -2.418e-04 3.180e-04 -0.760##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# reduced full model to test main effect of HTN
d_long_2 = d_long[!is.na(d_long$mean_sbp.),]
lmeFit.p.2 <- lmer(wb. ~ high_bp.*assessment + mean_sbp. + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long_2)
# reduced null model
lmeFit.p0.2 <- lmer(wb. ~ high_bp.*assessment + htn_meds_count. +
f.21003.0.0 + f.31.0.0 + angina.0 + heartattack.0 +
f.2443.0.0 + depr_l.0 + f.21001.0.0 + mean_hr.0 +
(1 | f.eid), data = d_long_2)
anova(lmeFit.p.2, lmeFit.p0.2) # main effect SBP p< 2.2e-16## refitting model(s) with ML (instead of REML)| npar | AIC | BIC | logLik | deviance | Chisq | Df | Pr(>Chisq) | |
|---|---|---|---|---|---|---|---|---|
| lmeFit.p0.2 | 17 | 228702.9 | 228870 | -114334.4 | 228668.9 | NA | NA | NA |
| lmeFit.p.2 | 18 | 228352.0 | 228529 | -114158.0 | 228316.0 | 352.856 | 1 | 0 |
summary(lmeFit.p.2)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## wb. ~ high_bp. * assessment + mean_sbp. + htn_meds_count. + f.21003.0.0 +
## f.31.0.0 + angina.0 + heartattack.0 + f.2443.0.0 + depr_l.0 +
## f.21001.0.0 + mean_hr.0 + (1 | f.eid)
## Data: d_long_2
##
## REML criterion at convergence: 228477.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7104 -0.3451 0.0118 0.3640 3.3060
##
## Random effects:
## Groups Name Variance Std.Dev.
## f.eid (Intercept) 0.21966 0.4687
## Residual 0.09331 0.3055
## Number of obs: 137384, groups: f.eid, 134504
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 3.756e+00 2.447e-02 153.477
## high_bp. -6.753e-02 4.209e-03 -16.046
## assessment2 1.233e-01 6.699e-03 18.410
## mean_sbp. 1.703e-03 9.058e-05 18.797
## htn_meds_count. 4.915e-03 2.239e-03 2.195
## f.21003.0.0 1.320e-02 2.093e-04 63.056
## f.31.0.0.L -2.155e-02 2.221e-03 -9.706
## angina.0 -1.467e-01 8.710e-03 -16.846
## heartattack.0 -7.890e-02 1.001e-02 -7.883
## f.2443.0.0.L 1.988e-01 3.838e-02 5.181
## f.2443.0.0.Q -6.984e-02 3.159e-02 -2.210
## f.2443.0.0.C -1.438e-01 2.285e-02 -6.293
## depr_l.0 -3.762e-01 5.984e-03 -62.857
## f.21001.0.0 -9.430e-03 3.325e-04 -28.360
## mean_hr.0 -2.441e-03 1.391e-04 -17.548
## high_bp.:assessment2 1.855e-02 1.230e-02 1.508##
## Correlation matrix not shown by default, as p = 16 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it